Kernels:

ggml-org
/

ggml-attn

Trusted publisher

Uploaded using `kernel-builder`.

by kernels-bot - opened 28 days ago

base: refs/heads/v1

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from: refs/pr/2

Discussion Files changed

+258

-18

Files changed (11) hide show

README.md +27 -0
build/torch213-metal-aarch64-darwin/__init__.py +16 -5
build/torch213-metal-aarch64-darwin/{_ggml_attn_metal_38609dd.abi3.so → _ggml_attn_metal_5d7b6e7.abi3.so} +2 -2
build/torch213-metal-aarch64-darwin/_ops.py +3 -3
build/torch213-metal-aarch64-darwin/metadata.json +8 -6
build/torch213-metal-aarch64-darwin/metadata.json.sigstore +1 -1
build/torch214-metal-aarch64-darwin/__init__.py +156 -0
build/torch214-metal-aarch64-darwin/_ggml_attn_metal_5d7b6e7.abi3.so +3 -0
build/torch214-metal-aarch64-darwin/_ops.py +9 -0
build/torch214-metal-aarch64-darwin/metadata.json +31 -0
build/torch214-metal-aarch64-darwin/metadata.json.sigstore +1 -0

README.md ADDED Viewed

	@@ -0,0 +1,27 @@

+---
+library_name: kernels
+license: mit
+---
+This is the repository card of ggml-org/ggml-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
+```python
+# make sure `kernels` is installed: `pip install -U kernels`
+from kernels import get_kernel
+kernel_module = get_kernel("ggml-org/ggml-attn", version=1)
+flash_attn = kernel_module.flash_attn
+flash_attn(...)
+```
+## Available functions
+- `flash_attn`
+- `supports_flash_attn`
+- `flash_attn_forward`
+## Benchmarks
+No benchmark available yet.

build/torch213-metal-aarch64-darwin/__init__.py CHANGED Viewed

@@ -29,27 +29,34 @@ def flash_attn(
     v: torch.Tensor,
     mask: torch.Tensor | None = None,
     scale: float | None = None,
 ) -> torch.Tensor:
     """Flash attention through ggml's vector kernel — the path upstream picks for decode.
     Args:
         q: `(n_seqs, n_heads, n_q, head_dim)`.
         k, v: `(n_seqs, n_heads_kv, n_kv, head_dim)`. Grouped-query attention is native, so do **not**
-            expand them to `n_heads` first — that copy is exactly what this avoids.
         mask: `(n_seqs, 1, n_q, n_kv)` additive mask, or None. Cast to f16 internally, as the kernel
             requires. **A None mask means attend to everything**: ggml has no `is_causal` argument, so
             causality has to arrive as a mask. `flash_attn_forward` builds one when it must.
         scale: softmax scale; defaults to `head_dim ** -0.5`.
     Returns:
-        `(n_seqs, n_q, n_heads, head_dim)` — tokens before heads, which is what SDPA gives after its
         own `.transpose(1, 2)`, so a caller usually wants precisely this and no further permute.
     Ask `supports_flash_attn` first: it answers for whichever of upstream's two paths the shape selects.
     """
     if scale is None:
         scale = q.shape[-1] ** -0.5
-    return ops.flash_attn(q, k, v, mask, scale)
 def supports_flash_attn(n_q: int, head_dim_k: int, head_dim_v: int) -> bool:
@@ -62,7 +69,7 @@ def supports_flash_attn(n_q: int, head_dim_k: int, head_dim_v: int) -> bool:
 @torch.library.register_fake(add_op_namespace_prefix("flash_attn"))
-def _(q, k, v, mask, scale):
     n_seqs, n_heads, n_q, _ = q.shape
     return q.new_empty((n_seqs, n_q, n_heads, v.shape[-1]))
@@ -142,4 +149,8 @@ def flash_attn_forward(
         mask = torch.zeros_like(mask, dtype=query.dtype).masked_fill_(~mask, float("-inf"))
     elif mask is None and n_q > 1:
         mask = _causal_mask(query, key.shape[2])
-    return flash_attn(query, key, value, mask, scaling), None

     v: torch.Tensor,
     mask: torch.Tensor | None = None,
     scale: float | None = None,
+    sinks: torch.Tensor | None = None,
+    softcap: float = 0.0,
 ) -> torch.Tensor:
     """Flash attention through ggml's vector kernel — the path upstream picks for decode.
     Args:
         q: `(n_seqs, n_heads, n_q, head_dim)`.
         k, v: `(n_seqs, n_heads_kv, n_kv, head_dim)`. Grouped-query attention is native, so do **not**
+            expand them to `n_heads` first — that copy is exactly what this avoids. An f16 cache is read
+            as f16, as llama.cpp reads its default cache; other dtypes are read as f32.
         mask: `(n_seqs, 1, n_q, n_kv)` additive mask, or None. Cast to f16 internally, as the kernel
             requires. **A None mask means attend to everything**: ggml has no `is_causal` argument, so
             causality has to arrive as a mask. `flash_attn_forward` builds one when it must.
         scale: softmax scale; defaults to `head_dim ** -0.5`.
+        sinks: attention sinks, one logit per query head that joins each row's softmax and takes no
+            value (gpt-oss's `s_aux`); upstream's `ggml_flash_attn_ext_add_sinks`.
+        softcap: logit softcapping, `softcap * tanh(scores / softcap)` before the softmax (Gemma 2's
+            `attn_logit_softcapping`); 0 for none.
     Returns:
+        `(n_seqs, n_q, n_heads, head_dim)` f32 — tokens before heads, which is what SDPA gives after its
         own `.transpose(1, 2)`, so a caller usually wants precisely this and no further permute.
     Ask `supports_flash_attn` first: it answers for whichever of upstream's two paths the shape selects.
     """
     if scale is None:
         scale = q.shape[-1] ** -0.5
+    return ops.flash_attn(q, k, v, mask, scale, sinks, softcap)
 def supports_flash_attn(n_q: int, head_dim_k: int, head_dim_v: int) -> bool:
 @torch.library.register_fake(add_op_namespace_prefix("flash_attn"))
+def _(q, k, v, mask, scale, sinks=None, softcap=0.0):
     n_seqs, n_heads, n_q, _ = q.shape
     return q.new_empty((n_seqs, n_q, n_heads, v.shape[-1]))
         mask = torch.zeros_like(mask, dtype=query.dtype).masked_fill_(~mask, float("-inf"))
     elif mask is None and n_q > 1:
         mask = _causal_mask(query, key.shape[2])
+    # transformers passes gpt-oss's sinks as `s_aux` and Gemma 2's softcapping as `softcap`
+    sinks = kwargs.get("s_aux")
+    softcap = kwargs.get("softcap") or 0.0
+    # ggml writes f32; hand the model back the dtype it computes in
+    return flash_attn(query, key, value, mask, scaling, sinks, softcap).to(query.dtype), None

build/torch213-metal-aarch64-darwin/{_ggml_attn_metal_38609dd.abi3.so → _ggml_attn_metal_5d7b6e7.abi3.so} RENAMED Viewed

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build/torch213-metal-aarch64-darwin/_ops.py CHANGED Viewed

@@ -1,9 +1,9 @@
 import torch
-from . import _ggml_attn_metal_38609dd
-ops = torch.ops._ggml_attn_metal_38609dd
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_ggml_attn_metal_38609dd::{op_name}"

 import torch
+from . import _ggml_attn_metal_5d7b6e7
+ops = torch.ops._ggml_attn_metal_5d7b6e7
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
+    return f"_ggml_attn_metal_5d7b6e7::{op_name}"

build/torch213-metal-aarch64-darwin/metadata.json CHANGED Viewed

@@ -1,28 +1,30 @@
 {
   "name": "ggml-attn",
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   "version": 1,
   "license": "MIT",
   "python-depends": [],
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     }
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     "kernel": {
-      "sha": "38609dd548a351e062a5810b4d5c50111a95e588",
       "dirty": false
     }
   }

 {
   "name": "ggml-attn",
+  "id": "_ggml_attn_metal_5d7b6e7",
   "version": 1,
+  "kernels-minver": "0.14.0",
   "license": "MIT",
   "python-depends": [],
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     "type": "metal"
   },
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     "kernel": {
+      "commit": "5d7b6e7e86f54333d0428c714dd419a668e6297d",
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build/torch214-metal-aarch64-darwin/__init__.py ADDED Viewed

	@@ -0,0 +1,156 @@

+"""ggml's flash attention as a torch op, and as a `transformers` attention implementation.
+This wraps `ggml_attn_ext` out of llama.cpp's Metal backend -- the same kernel llama.cpp runs. ggml
+picks between two implementations of that op by query count (`ne01 < 20` selects the *vector* path, wider
+`q` the *tiled* one); both are ported, so decode and prefill both run on ggml's kernels. Head-dim pairs
+upstream has no template for are refused, not silently served by torch's attention -- ask
+`supports_flash_attn` before selecting this implementation.
+"""
+import os
+from pathlib import Path
+import torch
+# A local (non-nix) build leaves the metallib on disk beside this module instead of embedding it, and
+# `ggml_dispatch.mm` looks it up through this variable when nothing is embedded. Set before the extension
+# is imported, and never over an existing value, so an explicit choice still wins.
+_METALLIB = Path(__file__).parent / "ggml-metal.metallib"
+if _METALLIB.is_file():
+    os.environ.setdefault("GGML_ATTN_METALLIB", str(_METALLIB))
+from ._ops import add_op_namespace_prefix, ops  # noqa: E402
+def flash_attn(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    mask: torch.Tensor | None = None,
+    scale: float | None = None,
+    sinks: torch.Tensor | None = None,
+    softcap: float = 0.0,
+) -> torch.Tensor:
+    """Flash attention through ggml's vector kernel — the path upstream picks for decode.
+    Args:
+        q: `(n_seqs, n_heads, n_q, head_dim)`.
+        k, v: `(n_seqs, n_heads_kv, n_kv, head_dim)`. Grouped-query attention is native, so do **not**
+            expand them to `n_heads` first — that copy is exactly what this avoids. An f16 cache is read
+            as f16, as llama.cpp reads its default cache; other dtypes are read as f32.
+        mask: `(n_seqs, 1, n_q, n_kv)` additive mask, or None. Cast to f16 internally, as the kernel
+            requires. **A None mask means attend to everything**: ggml has no `is_causal` argument, so
+            causality has to arrive as a mask. `flash_attn_forward` builds one when it must.
+        scale: softmax scale; defaults to `head_dim ** -0.5`.
+        sinks: attention sinks, one logit per query head that joins each row's softmax and takes no
+            value (gpt-oss's `s_aux`); upstream's `ggml_flash_attn_ext_add_sinks`.
+        softcap: logit softcapping, `softcap * tanh(scores / softcap)` before the softmax (Gemma 2's
+            `attn_logit_softcapping`); 0 for none.
+    Returns:
+        `(n_seqs, n_q, n_heads, head_dim)` f32 — tokens before heads, which is what SDPA gives after its
+        own `.transpose(1, 2)`, so a caller usually wants precisely this and no further permute.
+    Ask `supports_flash_attn` first: it answers for whichever of upstream's two paths the shape selects.
+    """
+    if scale is None:
+        scale = q.shape[-1] ** -0.5
+    return ops.flash_attn(q, k, v, mask, scale, sinks, softcap)
+def supports_flash_attn(n_q: int, head_dim_k: int, head_dim_v: int) -> bool:
+    """Whether this build has a flash-attention kernel for these shapes.
+    Upstream picks its vector kernel for `n_q < 20` and its tiled one above that; both are ported, so
+    what is declined is head-dim pairs neither has a template for, and `head_dim_k < head_dim_v`.
+    """
+    return ops.supports_flash_attn(n_q, head_dim_k, head_dim_v)
+@torch.library.register_fake(add_op_namespace_prefix("flash_attn"))
+def _(q, k, v, mask, scale, sinks=None, softcap=0.0):
+    n_seqs, n_heads, n_q, _ = q.shape
+    return q.new_empty((n_seqs, n_q, n_heads, v.shape[-1]))
+__all__ = ["flash_attn", "supports_flash_attn", "flash_attn_forward"]
+_CAUSAL_CACHE: dict = {}
+def _causal_mask(query: torch.Tensor, n_kv: int) -> torch.Tensor:
+    """The additive causal mask ggml requires when the caller supplied none.
+    `ggml_attn_ext` takes no `is_causal` argument: in ggml the mask *is* the causality, and a null mask
+    means attend to everything. `transformers` is entitled to hand us None -- `sdpa` carries causality in its
+    own `is_causal` flag instead, so the mask is dropped as an optimisation -- and that flag does not reach an
+    attention function at all. Rebuilding the mask here restores what was dropped; without it, any `n_q > 1`
+    call attends to future positions, which is silent: the logits move but greedy text often does not.
+    Lower-right aligned, matching `sdpa`'s convention when a cache is in play: the `n_q` queries are the last
+    `n_q` of the `n_kv` positions, so query `i` may see key `j <= i + (n_kv - n_q)`.
+    """
+    n_q = query.shape[2]
+    key = (n_q, n_kv, str(query.device))
+    cached = _CAUSAL_CACHE.get(key)
+    if cached is None:
+        # f16, not the query dtype: the kernel casts an f32 mask to f16 anyway, and at prefill widths
+        # that cast is a second n_q*n_kv allocation per layer per forward -- enough to cost more than
+        # the kernel gains. One entry, because a generation reuses one prefill shape and then decodes.
+        future = torch.ones(n_q, n_kv, dtype=torch.bool, device=query.device).triu(1 + n_kv - n_q)
+        cached = torch.zeros(n_q, n_kv, dtype=torch.float16, device=query.device).masked_fill_(
+            future, float("-inf")
+        )[None, None]
+        _CAUSAL_CACHE.clear()
+        _CAUSAL_CACHE[key] = cached
+    return cached
+def flash_attn_forward(
+    module: torch.nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor | None = None,
+    scaling: float | None = None,
+    dropout: float = 0.0,
+    **kwargs,
+) -> tuple[torch.Tensor, None]:
+    """Attention entry point shaped for `transformers`' attention interface.
+    Selected with `attn_implementation="<repo>:flash_attn_forward"`, which is how a kernel provides an
+    attention implementation without the caller reaching into `ALL_ATTENTION_FUNCTIONS`.
+    Both of upstream's paths are ported -- the vector kernel for `n_q < 20`, the tiled one above it -- so
+    decode and prefill both run on ggml's kernels. A head-dim pair neither has a template for raises, the
+    way `flash_attention_2` and the block-sparse kernels do, rather than quietly running torch's attention
+    under a name that promises ggml's: what upstream instantiates does not depend on the input, so a model
+    whose head dims are unsupported is unsupported for its whole run, and saying so at the first forward
+    beats reporting a speedup that never happened. Grouped-query attention is native to the kernel, so
+    `key`/`value` keep their own head count.
+    """
+    if scaling is None:
+        scaling = query.shape[-1] ** -0.5
+    n_q, head_dim_k, head_dim_v = query.shape[2], query.shape[-1], value.shape[-1]
+    if not supports_flash_attn(n_q, head_dim_k, head_dim_v):
+        raise ValueError(
+            f"ggml-attn has no kernel for head_dim {head_dim_k}/{head_dim_v} at {n_q} queries. ggml "
+            f"instantiates its attention kernels per head-dim pair, and this build carries the pairs "
+            f"upstream provides templates for; `supports_flash_attn(n_q, head_dim_k, head_dim_v)` answers "
+            f"for any shape. Use `attn_implementation='sdpa'` for this model."
+        )
+    mask = attention_mask
+    if mask is not None and mask.dtype == torch.bool:
+        # The kernel wants an additive mask; a boolean one says which positions are allowed.
+        mask = torch.zeros_like(mask, dtype=query.dtype).masked_fill_(~mask, float("-inf"))
+    elif mask is None and n_q > 1:
+        mask = _causal_mask(query, key.shape[2])
+    # transformers passes gpt-oss's sinks as `s_aux` and Gemma 2's softcapping as `softcap`
+    sinks = kwargs.get("s_aux")
+    softcap = kwargs.get("softcap") or 0.0
+    # ggml writes f32; hand the model back the dtype it computes in
+    return flash_attn(query, key, value, mask, scaling, sinks, softcap).to(query.dtype), None

build/torch214-metal-aarch64-darwin/_ggml_attn_metal_5d7b6e7.abi3.so ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:4a7f22d893de3ae765267295b0199c9fdb15ea5138ac1eef68d518a79ac1c1e0
+size 9579184

build/torch214-metal-aarch64-darwin/_ops.py ADDED Viewed

	@@ -0,0 +1,9 @@

+import torch
+from . import _ggml_attn_metal_5d7b6e7
+ops = torch.ops._ggml_attn_metal_5d7b6e7
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_ggml_attn_metal_5d7b6e7::{op_name}"

build/torch214-metal-aarch64-darwin/metadata.json ADDED Viewed

	@@ -0,0 +1,31 @@

+{
+  "name": "ggml-attn",
+  "id": "_ggml_attn_metal_5d7b6e7",
+  "version": 1,
+  "kernels-minver": "0.14.0",
+  "license": "MIT",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "metal"
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
+      "__init__.py": "2zbEFDRRtbDKJLS22KAG0/SEWEJycxEgVfkBR2gpo+s=",
+      "_ggml_attn_metal_5d7b6e7.abi3.so": "Sn8i2JPeOudlJnKVsBmcn9sV6lE4rB7vaNUYp5rBweA=",
+      "_ops.py": "YynNnQ4xi51MpqejOgt86BWJgohmwvhxWXeoS5ptgcM="
+    }
+  },
+  "provenance": {
+    "kernel-builder": {
+      "version": "0.17.0-dev0",
+      "commit": "a7f0afdb29a6a3372b1d47180cc0c182454c5e3b",
+      "dirty": false
+    },
+    "kernel": {
+      "commit": "5d7b6e7e86f54333d0428c714dd419a668e6297d",
+      "dirty": false
+    }
+  }
+}

build/torch214-metal-aarch64-darwin/metadata.json.sigstore ADDED Viewed

	@@ -0,0 +1 @@

+ 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