Kernels:
Trusted publisher
Uploaded using `kernel-builder`.
#3
by kernels-bot - opened
- README.md +2 -2
- build/torch210-metal-aarch64-darwin/__init__.py +40 -0
- build/torch210-metal-aarch64-darwin/_ops.py +9 -0
- build/torch210-metal-aarch64-darwin/_topk_metal_fe2bb66.abi3.so +3 -0
- build/torch210-metal-aarch64-darwin/layers.py +39 -0
- build/torch210-metal-aarch64-darwin/metadata.json +33 -0
- build/torch210-metal-aarch64-darwin/metadata.json.sigstore +1 -0
- build/torch210-metal-aarch64-darwin/symbols.json +37 -0
- build/torch211-metal-aarch64-darwin/__init__.py +40 -0
- build/torch211-metal-aarch64-darwin/_ops.py +9 -0
- build/torch211-metal-aarch64-darwin/_topk_metal_fe2bb66.abi3.so +3 -0
- build/torch211-metal-aarch64-darwin/layers.py +39 -0
- build/torch211-metal-aarch64-darwin/metadata.json +33 -0
- build/torch211-metal-aarch64-darwin/metadata.json.sigstore +1 -0
- build/torch211-metal-aarch64-darwin/symbols.json +37 -0
- build/torch212-metal-aarch64-darwin/_ops.py +3 -3
- build/torch212-metal-aarch64-darwin/{_topk_metal_4487601.abi3.so → _topk_metal_fe2bb66.abi3.so} +1 -1
- build/torch212-metal-aarch64-darwin/metadata.json +10 -7
- build/torch212-metal-aarch64-darwin/metadata.json.sigstore +1 -1
- build/torch212-metal-aarch64-darwin/symbols.json +37 -0
- build/torch213-metal-aarch64-darwin/_ops.py +3 -3
- build/torch213-metal-aarch64-darwin/{_topk_metal_4487601.abi3.so → _topk_metal_fe2bb66.abi3.so} +1 -1
- build/torch213-metal-aarch64-darwin/metadata.json +10 -7
- build/torch213-metal-aarch64-darwin/metadata.json.sigstore +1 -1
- build/torch213-metal-aarch64-darwin/symbols.json +37 -0
- build/torch214-metal-aarch64-darwin/__init__.py +40 -0
- build/torch214-metal-aarch64-darwin/_ops.py +9 -0
- build/torch214-metal-aarch64-darwin/_topk_metal_fe2bb66.abi3.so +3 -0
- build/torch214-metal-aarch64-darwin/layers.py +39 -0
- build/torch214-metal-aarch64-darwin/metadata.json +33 -0
- build/torch214-metal-aarch64-darwin/metadata.json.sigstore +1 -0
- build/torch214-metal-aarch64-darwin/symbols.json +37 -0
- build/torch215-metal-aarch64-darwin/__init__.py +40 -0
- build/torch215-metal-aarch64-darwin/_ops.py +9 -0
- build/torch215-metal-aarch64-darwin/_topk_metal_fe2bb66.abi3.so +3 -0
- build/torch215-metal-aarch64-darwin/layers.py +39 -0
- build/torch215-metal-aarch64-darwin/metadata.json +33 -0
- build/torch215-metal-aarch64-darwin/metadata.json.sigstore +1 -0
- build/torch215-metal-aarch64-darwin/symbols.json +37 -0
README.md
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license: mit
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---
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-
This is the repository card of
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## How to use
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# make sure `kernels` is installed: `pip install -U kernels`
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from kernels import get_kernel
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kernel_module = get_kernel("
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SoftmaxTopKRouter = kernel_module.SoftmaxTopKRouter
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SoftmaxTopKRouter(...)
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license: mit
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---
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This is the repository card of transformers-community/topk 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.
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## How to use
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# make sure `kernels` is installed: `pip install -U kernels`
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from kernels import get_kernel
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kernel_module = get_kernel("transformers-community/topk", version=1)
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SoftmaxTopKRouter = kernel_module.SoftmaxTopKRouter
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SoftmaxTopKRouter(...)
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build/torch210-metal-aarch64-darwin/__init__.py
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"""Top-k over a small row, as a torch op.
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For a MoE router. torch's MPS `topk` is a full sort, and so is ggml's -- `GGML_OP_TOP_K` dispatches
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`kernel_argsort_f32_i32_desc` -- which is why this is written rather than ported: selecting the
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largest 8 of 256 logits does not need the row ordered. 26us against 71us, once per layer per token.
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"""
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import os
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from pathlib import Path
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import torch
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# A local (non-nix) build leaves the metallib on disk beside this module instead of embedding it, and
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# `dispatch.mm` looks it up through this variable when nothing is embedded. Set before the extension
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# is imported, and never over an existing value, so an explicit choice still wins.
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_METALLIB = Path(__file__).parent / "ggml-metal.metallib"
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if _METALLIB.is_file():
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os.environ.setdefault("TOPK_METALLIB", str(_METALLIB))
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from ._ops import add_op_namespace_prefix, ops # noqa: E402
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from .layers import SoftmaxTopKRouter # noqa: E402,F401
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def top_k(logits: torch.Tensor, k: int, softmax: bool = False):
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"""`(values, indices)` of the largest `k` per row of a 2D f32 tensor.
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`indices` is int32, which is what an expert-routed matmul wants, so routing them onward costs no
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cast. With `softmax`, `values` is softmaxed over the selected k.
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"""
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return ops.top_k(logits, k, softmax)
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@torch.library.register_fake(add_op_namespace_prefix("top_k"))
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def _top_k_fake(logits, k, softmax):
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rows = logits.shape[0]
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return [logits.new_empty((rows, k)), logits.new_empty((rows, k), dtype=torch.int32)]
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__all__ = ["SoftmaxTopKRouter", "top_k"]
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build/torch210-metal-aarch64-darwin/_ops.py
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import torch
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from . import _topk_metal_fe2bb66
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ops = torch.ops._topk_metal_fe2bb66
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def add_op_namespace_prefix(op_name: str):
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"""
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Prefix op by namespace.
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"""
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return f"_topk_metal_fe2bb66::{op_name}"
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build/torch210-metal-aarch64-darwin/_topk_metal_fe2bb66.abi3.so
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version https://git-lfs.github.com/spec/v1
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oid sha256:696ca60a2f35db6d84db00ba9d83250161435b3e10a003a2a3346b6ae636565c
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size 131064
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build/torch210-metal-aarch64-darwin/layers.py
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"""Layer-level entry points, for `kernels`' layer mapping.
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`transformers` marks the layers a kernel may replace with `@use_kernel_forward_from_hub("<name>")`, and a
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kernel repository supplies a class of that name whose `forward` is grafted onto the real module. The module
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keeps its own parameters and constructor -- the class here is stateless, which `kernels` enforces: no
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`__init__`, no members besides `forward`, and a signature matching the layer being replaced.
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Named `SoftmaxTopKRouter` rather than `TopKRouter` because the rewrite below is only valid for routers
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that score with a softmax. One that scores with a sigmoid returns the same shapes and would graft
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cleanly onto this, then be quietly wrong.
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from ._ops import ops
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class SoftmaxTopKRouter(nn.Module):
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"""A softmax router's top-k, in two dispatches instead of four.
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The model softmaxes over every expert, takes the top `k`, then renormalizes those `k`. Softmax is
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monotonic, so the top `k` probabilities belong to the top `k` logits, and renormalizing them is a
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softmax over just those logits -- `exp(x_i) / sum_{j in topk} exp(x_j)` either way. So the whole
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tail is skipped: select first, then softmax the `k` that survived, which the kernel does in the
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same pass.
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Worth it because a MoE decode step is dispatch-bound rather than arithmetic-bound: this is 40
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layers x 2 fewer operations, and the top-k itself stops being a full sort.
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`indices` comes back int32, which is what an expert-routed matmul wants.
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"""
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def forward(self, hidden_states: torch.Tensor):
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hidden_states = hidden_states.reshape(-1, self.hidden_dim)
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router_logits = F.linear(hidden_states, self.weight)
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scores, indices = ops.top_k(router_logits, self.top_k, True)
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return router_logits, scores.to(router_logits.dtype), indices
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build/torch210-metal-aarch64-darwin/metadata.json
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{
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"name": "topk",
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"id": "_topk_metal_fe2bb66",
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"version": 1,
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"kernels-minver": "0.14.0",
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"license": "MIT",
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"python-depends": [],
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"kernel-depends": [],
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"backend": {
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"type": "metal"
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},
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"digest": {
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"algorithm": "sha256",
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"files": {
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"__init__.py": "cm0ZJnxWpFzy3M3RrxrS6q9T1otdIBa+Npd7kteAZbg=",
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"_ops.py": "r7EEKA36ZAicnt9YXZ1CZ8k5OkzXbWUk3oowDZI1L6A=",
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"_topk_metal_fe2bb66.abi3.so": "aWymCi81222E2wC6nYMlAWFDWz4QoAOiozRrauY2Vlw=",
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"layers.py": "DP5wFLC5D7eeoDBuFY9Eoi36CPOjreKcQJk1AF+eRHg=",
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"symbols.json": "gLRlxKtWt92J4oVk9FQdvL89CipliDHfkh6yTVY1WZ0="
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}
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},
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"provenance": {
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"kernel-builder": {
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"version": "0.18.0-dev0",
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"commit": "d054906a13710b811b96697b4635f03020842733",
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"dirty": false
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},
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"kernel": {
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"commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
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"dirty": false
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}
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}
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}
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build/torch210-metal-aarch64-darwin/metadata.json.sigstore
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| 1 |
+
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|
build/torch210-metal-aarch64-darwin/symbols.json
ADDED
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@@ -0,0 +1,37 @@
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| 1 |
+
{
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| 2 |
+
"schema_version": 1,
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| 3 |
+
"module": "_topk_metal_fe2bb66",
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| 4 |
+
"functions": [
|
| 5 |
+
{
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| 6 |
+
"name": "top_k",
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| 7 |
+
"qualname": "top_k",
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| 8 |
+
"module": "_topk_metal_fe2bb66",
|
| 9 |
+
"kind": "function",
|
| 10 |
+
"doc": "`(values, indices)` of the largest `k` per row of a 2D f32 tensor.\n\n`indices` is int32, which is what an expert-routed matmul wants, so routing them onward costs no\ncast. With `softmax`, `values` is softmaxed over the selected k.",
|
| 11 |
+
"signature": {
|
| 12 |
+
"parameters": [
|
| 13 |
+
{
|
| 14 |
+
"name": "logits",
|
| 15 |
+
"kind": "POSITIONAL_OR_KEYWORD",
|
| 16 |
+
"default": null,
|
| 17 |
+
"annotation": "torch.Tensor"
|
| 18 |
+
},
|
| 19 |
+
{
|
| 20 |
+
"name": "k",
|
| 21 |
+
"kind": "POSITIONAL_OR_KEYWORD",
|
| 22 |
+
"default": null,
|
| 23 |
+
"annotation": "int"
|
| 24 |
+
},
|
| 25 |
+
{
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| 26 |
+
"name": "softmax",
|
| 27 |
+
"kind": "POSITIONAL_OR_KEYWORD",
|
| 28 |
+
"default": "False",
|
| 29 |
+
"annotation": "bool"
|
| 30 |
+
}
|
| 31 |
+
],
|
| 32 |
+
"return_annotation": null
|
| 33 |
+
}
|
| 34 |
+
}
|
| 35 |
+
],
|
| 36 |
+
"layers": []
|
| 37 |
+
}
|
build/torch211-metal-aarch64-darwin/__init__.py
ADDED
|
@@ -0,0 +1,40 @@
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|
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|
|
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|
|
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|
|
|
|
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|
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|
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|
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|
|
|
|
|
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|
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|
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|
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|
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|
|
|
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|
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|
|
|
|
|
|
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|
|
|
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|
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|
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|
|
|
|
|
|
|
|
| 1 |
+
"""Top-k over a small row, as a torch op.
|
| 2 |
+
|
| 3 |
+
For a MoE router. torch's MPS `topk` is a full sort, and so is ggml's -- `GGML_OP_TOP_K` dispatches
|
| 4 |
+
`kernel_argsort_f32_i32_desc` -- which is why this is written rather than ported: selecting the
|
| 5 |
+
largest 8 of 256 logits does not need the row ordered. 26us against 71us, once per layer per token.
|
| 6 |
+
"""
|
| 7 |
+
|
| 8 |
+
import os
|
| 9 |
+
from pathlib import Path
|
| 10 |
+
|
| 11 |
+
import torch
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
# A local (non-nix) build leaves the metallib on disk beside this module instead of embedding it, and
|
| 15 |
+
# `dispatch.mm` looks it up through this variable when nothing is embedded. Set before the extension
|
| 16 |
+
# is imported, and never over an existing value, so an explicit choice still wins.
|
| 17 |
+
_METALLIB = Path(__file__).parent / "ggml-metal.metallib"
|
| 18 |
+
if _METALLIB.is_file():
|
| 19 |
+
os.environ.setdefault("TOPK_METALLIB", str(_METALLIB))
|
| 20 |
+
|
| 21 |
+
from ._ops import add_op_namespace_prefix, ops # noqa: E402
|
| 22 |
+
from .layers import SoftmaxTopKRouter # noqa: E402,F401
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
def top_k(logits: torch.Tensor, k: int, softmax: bool = False):
|
| 26 |
+
"""`(values, indices)` of the largest `k` per row of a 2D f32 tensor.
|
| 27 |
+
|
| 28 |
+
`indices` is int32, which is what an expert-routed matmul wants, so routing them onward costs no
|
| 29 |
+
cast. With `softmax`, `values` is softmaxed over the selected k.
|
| 30 |
+
"""
|
| 31 |
+
return ops.top_k(logits, k, softmax)
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
@torch.library.register_fake(add_op_namespace_prefix("top_k"))
|
| 35 |
+
def _top_k_fake(logits, k, softmax):
|
| 36 |
+
rows = logits.shape[0]
|
| 37 |
+
return [logits.new_empty((rows, k)), logits.new_empty((rows, k), dtype=torch.int32)]
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
__all__ = ["SoftmaxTopKRouter", "top_k"]
|
build/torch211-metal-aarch64-darwin/_ops.py
ADDED
|
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import torch
|
| 2 |
+
from . import _topk_metal_fe2bb66
|
| 3 |
+
ops = torch.ops._topk_metal_fe2bb66
|
| 4 |
+
|
| 5 |
+
def add_op_namespace_prefix(op_name: str):
|
| 6 |
+
"""
|
| 7 |
+
Prefix op by namespace.
|
| 8 |
+
"""
|
| 9 |
+
return f"_topk_metal_fe2bb66::{op_name}"
|
build/torch211-metal-aarch64-darwin/_topk_metal_fe2bb66.abi3.so
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4e3da2f94976a36f95248b4ea71531da0016805bacf214c6c68bd87ac9d36cfb
|
| 3 |
+
size 131064
|
build/torch211-metal-aarch64-darwin/layers.py
ADDED
|
@@ -0,0 +1,39 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""Layer-level entry points, for `kernels`' layer mapping.
|
| 2 |
+
|
| 3 |
+
`transformers` marks the layers a kernel may replace with `@use_kernel_forward_from_hub("<name>")`, and a
|
| 4 |
+
kernel repository supplies a class of that name whose `forward` is grafted onto the real module. The module
|
| 5 |
+
keeps its own parameters and constructor -- the class here is stateless, which `kernels` enforces: no
|
| 6 |
+
`__init__`, no members besides `forward`, and a signature matching the layer being replaced.
|
| 7 |
+
|
| 8 |
+
Named `SoftmaxTopKRouter` rather than `TopKRouter` because the rewrite below is only valid for routers
|
| 9 |
+
that score with a softmax. One that scores with a sigmoid returns the same shapes and would graft
|
| 10 |
+
cleanly onto this, then be quietly wrong.
|
| 11 |
+
"""
|
| 12 |
+
|
| 13 |
+
import torch
|
| 14 |
+
import torch.nn as nn
|
| 15 |
+
import torch.nn.functional as F
|
| 16 |
+
|
| 17 |
+
from ._ops import ops
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
class SoftmaxTopKRouter(nn.Module):
|
| 21 |
+
"""A softmax router's top-k, in two dispatches instead of four.
|
| 22 |
+
|
| 23 |
+
The model softmaxes over every expert, takes the top `k`, then renormalizes those `k`. Softmax is
|
| 24 |
+
monotonic, so the top `k` probabilities belong to the top `k` logits, and renormalizing them is a
|
| 25 |
+
softmax over just those logits -- `exp(x_i) / sum_{j in topk} exp(x_j)` either way. So the whole
|
| 26 |
+
tail is skipped: select first, then softmax the `k` that survived, which the kernel does in the
|
| 27 |
+
same pass.
|
| 28 |
+
|
| 29 |
+
Worth it because a MoE decode step is dispatch-bound rather than arithmetic-bound: this is 40
|
| 30 |
+
layers x 2 fewer operations, and the top-k itself stops being a full sort.
|
| 31 |
+
|
| 32 |
+
`indices` comes back int32, which is what an expert-routed matmul wants.
|
| 33 |
+
"""
|
| 34 |
+
|
| 35 |
+
def forward(self, hidden_states: torch.Tensor):
|
| 36 |
+
hidden_states = hidden_states.reshape(-1, self.hidden_dim)
|
| 37 |
+
router_logits = F.linear(hidden_states, self.weight)
|
| 38 |
+
scores, indices = ops.top_k(router_logits, self.top_k, True)
|
| 39 |
+
return router_logits, scores.to(router_logits.dtype), indices
|
build/torch211-metal-aarch64-darwin/metadata.json
ADDED
|
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"name": "topk",
|
| 3 |
+
"id": "_topk_metal_fe2bb66",
|
| 4 |
+
"version": 1,
|
| 5 |
+
"kernels-minver": "0.14.0",
|
| 6 |
+
"license": "MIT",
|
| 7 |
+
"python-depends": [],
|
| 8 |
+
"kernel-depends": [],
|
| 9 |
+
"backend": {
|
| 10 |
+
"type": "metal"
|
| 11 |
+
},
|
| 12 |
+
"digest": {
|
| 13 |
+
"algorithm": "sha256",
|
| 14 |
+
"files": {
|
| 15 |
+
"__init__.py": "cm0ZJnxWpFzy3M3RrxrS6q9T1otdIBa+Npd7kteAZbg=",
|
| 16 |
+
"_ops.py": "r7EEKA36ZAicnt9YXZ1CZ8k5OkzXbWUk3oowDZI1L6A=",
|
| 17 |
+
"_topk_metal_fe2bb66.abi3.so": "Tj2i+Ul2o2+VJItOpxUx2gAWgFus8hTGxovYesnTbPs=",
|
| 18 |
+
"layers.py": "DP5wFLC5D7eeoDBuFY9Eoi36CPOjreKcQJk1AF+eRHg=",
|
| 19 |
+
"symbols.json": "gLRlxKtWt92J4oVk9FQdvL89CipliDHfkh6yTVY1WZ0="
|
| 20 |
+
}
|
| 21 |
+
},
|
| 22 |
+
"provenance": {
|
| 23 |
+
"kernel-builder": {
|
| 24 |
+
"version": "0.18.0-dev0",
|
| 25 |
+
"commit": "d054906a13710b811b96697b4635f03020842733",
|
| 26 |
+
"dirty": false
|
| 27 |
+
},
|
| 28 |
+
"kernel": {
|
| 29 |
+
"commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
|
| 30 |
+
"dirty": false
|
| 31 |
+
}
|
| 32 |
+
}
|
| 33 |
+
}
|
build/torch211-metal-aarch64-darwin/metadata.json.sigstore
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
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|
build/torch211-metal-aarch64-darwin/symbols.json
ADDED
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+
{
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| 2 |
+
"schema_version": 1,
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| 3 |
+
"module": "_topk_metal_fe2bb66",
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| 4 |
+
"functions": [
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| 5 |
+
{
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| 6 |
+
"name": "top_k",
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| 7 |
+
"qualname": "top_k",
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| 8 |
+
"module": "_topk_metal_fe2bb66",
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| 9 |
+
"kind": "function",
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| 10 |
+
"doc": "`(values, indices)` of the largest `k` per row of a 2D f32 tensor.\n\n`indices` is int32, which is what an expert-routed matmul wants, so routing them onward costs no\ncast. With `softmax`, `values` is softmaxed over the selected k.",
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| 11 |
+
"signature": {
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| 12 |
+
"parameters": [
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| 13 |
+
{
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| 14 |
+
"name": "logits",
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| 15 |
+
"kind": "POSITIONAL_OR_KEYWORD",
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| 16 |
+
"default": null,
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| 17 |
+
"annotation": "torch.Tensor"
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| 18 |
+
},
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| 19 |
+
{
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| 20 |
+
"name": "k",
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| 21 |
+
"kind": "POSITIONAL_OR_KEYWORD",
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| 22 |
+
"default": null,
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| 23 |
+
"annotation": "int"
|
| 24 |
+
},
|
| 25 |
+
{
|
| 26 |
+
"name": "softmax",
|
| 27 |
+
"kind": "POSITIONAL_OR_KEYWORD",
|
| 28 |
+
"default": "False",
|
| 29 |
+
"annotation": "bool"
|
| 30 |
+
}
|
| 31 |
+
],
|
| 32 |
+
"return_annotation": null
|
| 33 |
+
}
|
| 34 |
+
}
|
| 35 |
+
],
|
| 36 |
+
"layers": []
|
| 37 |
+
}
|
build/torch212-metal-aarch64-darwin/_ops.py
CHANGED
|
@@ -1,9 +1,9 @@
|
|
| 1 |
import torch
|
| 2 |
-
from . import
|
| 3 |
-
ops = torch.ops.
|
| 4 |
|
| 5 |
def add_op_namespace_prefix(op_name: str):
|
| 6 |
"""
|
| 7 |
Prefix op by namespace.
|
| 8 |
"""
|
| 9 |
-
return f"
|
|
|
|
| 1 |
import torch
|
| 2 |
+
from . import _topk_metal_fe2bb66
|
| 3 |
+
ops = torch.ops._topk_metal_fe2bb66
|
| 4 |
|
| 5 |
def add_op_namespace_prefix(op_name: str):
|
| 6 |
"""
|
| 7 |
Prefix op by namespace.
|
| 8 |
"""
|
| 9 |
+
return f"_topk_metal_fe2bb66::{op_name}"
|
build/torch212-metal-aarch64-darwin/{_topk_metal_4487601.abi3.so → _topk_metal_fe2bb66.abi3.so}
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
-
oid sha256:
|
| 3 |
size 131048
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:55adf3a80eb5ab24278c4eaa1770921b96b781ff6de297ebe5f51817ea38b58c
|
| 3 |
size 131048
|
build/torch212-metal-aarch64-darwin/metadata.json
CHANGED
|
@@ -1,9 +1,11 @@
|
|
| 1 |
{
|
| 2 |
"name": "topk",
|
| 3 |
-
"id": "
|
| 4 |
"version": 1,
|
|
|
|
| 5 |
"license": "MIT",
|
| 6 |
"python-depends": [],
|
|
|
|
| 7 |
"backend": {
|
| 8 |
"type": "metal"
|
| 9 |
},
|
|
@@ -11,19 +13,20 @@
|
|
| 11 |
"algorithm": "sha256",
|
| 12 |
"files": {
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| 13 |
"__init__.py": "cm0ZJnxWpFzy3M3RrxrS6q9T1otdIBa+Npd7kteAZbg=",
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"_ops.py": "
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"
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"layers.py": "DP5wFLC5D7eeoDBuFY9Eoi36CPOjreKcQJk1AF+eRHg="
|
|
|
|
| 17 |
}
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| 18 |
},
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| 19 |
"provenance": {
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| 20 |
"kernel-builder": {
|
| 21 |
-
"version": "0.
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| 22 |
-
"
|
| 23 |
"dirty": false
|
| 24 |
},
|
| 25 |
"kernel": {
|
| 26 |
-
"
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| 27 |
"dirty": false
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| 28 |
}
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| 29 |
}
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|
|
|
| 1 |
{
|
| 2 |
"name": "topk",
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| 3 |
+
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|
| 4 |
"version": 1,
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+
"kernels-minver": "0.14.0",
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| 6 |
"license": "MIT",
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| 7 |
"python-depends": [],
|
| 8 |
+
"kernel-depends": [],
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| 9 |
"backend": {
|
| 10 |
"type": "metal"
|
| 11 |
},
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|
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|
| 13 |
"algorithm": "sha256",
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| 14 |
"files": {
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| 15 |
"__init__.py": "cm0ZJnxWpFzy3M3RrxrS6q9T1otdIBa+Npd7kteAZbg=",
|
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+
"_ops.py": "r7EEKA36ZAicnt9YXZ1CZ8k5OkzXbWUk3oowDZI1L6A=",
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"_topk_metal_fe2bb66.abi3.so": "Va3zqA61qyQnjE6qF3CSG5a3gf9t4pfr5fUYF+o4tYw=",
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"layers.py": "DP5wFLC5D7eeoDBuFY9Eoi36CPOjreKcQJk1AF+eRHg=",
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"symbols.json": "gLRlxKtWt92J4oVk9FQdvL89CipliDHfkh6yTVY1WZ0="
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| 20 |
}
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| 21 |
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"provenance": {
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"kernel-builder": {
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| 24 |
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| 25 |
+
"commit": "d054906a13710b811b96697b4635f03020842733",
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| 26 |
"dirty": false
|
| 27 |
},
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| 28 |
"kernel": {
|
| 29 |
+
"commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
|
| 30 |
"dirty": false
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| 31 |
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| 32 |
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build/torch212-metal-aarch64-darwin/metadata.json.sigstore
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|
build/torch212-metal-aarch64-darwin/symbols.json
ADDED
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| 1 |
+
{
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| 2 |
+
"schema_version": 1,
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| 3 |
+
"module": "_topk_metal_fe2bb66",
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| 4 |
+
"functions": [
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| 5 |
+
{
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| 6 |
+
"name": "top_k",
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| 7 |
+
"qualname": "top_k",
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| 8 |
+
"module": "_topk_metal_fe2bb66",
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| 9 |
+
"kind": "function",
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| 10 |
+
"doc": "`(values, indices)` of the largest `k` per row of a 2D f32 tensor.\n\n`indices` is int32, which is what an expert-routed matmul wants, so routing them onward costs no\ncast. With `softmax`, `values` is softmaxed over the selected k.",
|
| 11 |
+
"signature": {
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| 12 |
+
"parameters": [
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| 13 |
+
{
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| 14 |
+
"name": "logits",
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| 15 |
+
"kind": "POSITIONAL_OR_KEYWORD",
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| 16 |
+
"default": null,
|
| 17 |
+
"annotation": "torch.Tensor"
|
| 18 |
+
},
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| 19 |
+
{
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| 20 |
+
"name": "k",
|
| 21 |
+
"kind": "POSITIONAL_OR_KEYWORD",
|
| 22 |
+
"default": null,
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| 23 |
+
"annotation": "int"
|
| 24 |
+
},
|
| 25 |
+
{
|
| 26 |
+
"name": "softmax",
|
| 27 |
+
"kind": "POSITIONAL_OR_KEYWORD",
|
| 28 |
+
"default": "False",
|
| 29 |
+
"annotation": "bool"
|
| 30 |
+
}
|
| 31 |
+
],
|
| 32 |
+
"return_annotation": null
|
| 33 |
+
}
|
| 34 |
+
}
|
| 35 |
+
],
|
| 36 |
+
"layers": []
|
| 37 |
+
}
|
build/torch213-metal-aarch64-darwin/_ops.py
CHANGED
|
@@ -1,9 +1,9 @@
|
|
| 1 |
import torch
|
| 2 |
-
from . import
|
| 3 |
-
ops = torch.ops.
|
| 4 |
|
| 5 |
def add_op_namespace_prefix(op_name: str):
|
| 6 |
"""
|
| 7 |
Prefix op by namespace.
|
| 8 |
"""
|
| 9 |
-
return f"
|
|
|
|
| 1 |
import torch
|
| 2 |
+
from . import _topk_metal_fe2bb66
|
| 3 |
+
ops = torch.ops._topk_metal_fe2bb66
|
| 4 |
|
| 5 |
def add_op_namespace_prefix(op_name: str):
|
| 6 |
"""
|
| 7 |
Prefix op by namespace.
|
| 8 |
"""
|
| 9 |
+
return f"_topk_metal_fe2bb66::{op_name}"
|
build/torch213-metal-aarch64-darwin/{_topk_metal_4487601.abi3.so → _topk_metal_fe2bb66.abi3.so}
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 131048
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:7bce19b17a7995b2aed5d13e9c96e4cb22c4766f38fcdc989835ff7bf3330aaa
|
| 3 |
size 131048
|
build/torch213-metal-aarch64-darwin/metadata.json
CHANGED
|
@@ -1,9 +1,11 @@
|
|
| 1 |
{
|
| 2 |
"name": "topk",
|
| 3 |
-
"id": "
|
| 4 |
"version": 1,
|
|
|
|
| 5 |
"license": "MIT",
|
| 6 |
"python-depends": [],
|
|
|
|
| 7 |
"backend": {
|
| 8 |
"type": "metal"
|
| 9 |
},
|
|
@@ -11,19 +13,20 @@
|
|
| 11 |
"algorithm": "sha256",
|
| 12 |
"files": {
|
| 13 |
"__init__.py": "cm0ZJnxWpFzy3M3RrxrS6q9T1otdIBa+Npd7kteAZbg=",
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| 14 |
-
"_ops.py": "
|
| 15 |
-
"
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| 16 |
-
"layers.py": "DP5wFLC5D7eeoDBuFY9Eoi36CPOjreKcQJk1AF+eRHg="
|
|
|
|
| 17 |
}
|
| 18 |
},
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| 19 |
"provenance": {
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| 20 |
"kernel-builder": {
|
| 21 |
-
"version": "0.
|
| 22 |
-
"
|
| 23 |
"dirty": false
|
| 24 |
},
|
| 25 |
"kernel": {
|
| 26 |
-
"
|
| 27 |
"dirty": false
|
| 28 |
}
|
| 29 |
}
|
|
|
|
| 1 |
{
|
| 2 |
"name": "topk",
|
| 3 |
+
"id": "_topk_metal_fe2bb66",
|
| 4 |
"version": 1,
|
| 5 |
+
"kernels-minver": "0.14.0",
|
| 6 |
"license": "MIT",
|
| 7 |
"python-depends": [],
|
| 8 |
+
"kernel-depends": [],
|
| 9 |
"backend": {
|
| 10 |
"type": "metal"
|
| 11 |
},
|
|
|
|
| 13 |
"algorithm": "sha256",
|
| 14 |
"files": {
|
| 15 |
"__init__.py": "cm0ZJnxWpFzy3M3RrxrS6q9T1otdIBa+Npd7kteAZbg=",
|
| 16 |
+
"_ops.py": "r7EEKA36ZAicnt9YXZ1CZ8k5OkzXbWUk3oowDZI1L6A=",
|
| 17 |
+
"_topk_metal_fe2bb66.abi3.so": "e84ZsXp5lbKu1dE+nJbkyyLEdm84/NyYmDX/e/MzCqo=",
|
| 18 |
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"layers.py": "DP5wFLC5D7eeoDBuFY9Eoi36CPOjreKcQJk1AF+eRHg=",
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"symbols.json": "gLRlxKtWt92J4oVk9FQdvL89CipliDHfkh6yTVY1WZ0="
|
| 20 |
}
|
| 21 |
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| 22 |
"provenance": {
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| 23 |
"kernel-builder": {
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| 24 |
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"version": "0.18.0-dev0",
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| 25 |
+
"commit": "d054906a13710b811b96697b4635f03020842733",
|
| 26 |
"dirty": false
|
| 27 |
},
|
| 28 |
"kernel": {
|
| 29 |
+
"commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
|
| 30 |
"dirty": false
|
| 31 |
}
|
| 32 |
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build/torch213-metal-aarch64-darwin/metadata.json.sigstore
CHANGED
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|
build/torch213-metal-aarch64-darwin/symbols.json
ADDED
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+
{
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| 2 |
+
"schema_version": 1,
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| 3 |
+
"module": "_topk_metal_fe2bb66",
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| 4 |
+
"functions": [
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| 5 |
+
{
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| 6 |
+
"name": "top_k",
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| 7 |
+
"qualname": "top_k",
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| 8 |
+
"module": "_topk_metal_fe2bb66",
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| 9 |
+
"kind": "function",
|
| 10 |
+
"doc": "`(values, indices)` of the largest `k` per row of a 2D f32 tensor.\n\n`indices` is int32, which is what an expert-routed matmul wants, so routing them onward costs no\ncast. With `softmax`, `values` is softmaxed over the selected k.",
|
| 11 |
+
"signature": {
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| 12 |
+
"parameters": [
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| 13 |
+
{
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| 14 |
+
"name": "logits",
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| 15 |
+
"kind": "POSITIONAL_OR_KEYWORD",
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| 16 |
+
"default": null,
|
| 17 |
+
"annotation": "torch.Tensor"
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| 18 |
+
},
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| 19 |
+
{
|
| 20 |
+
"name": "k",
|
| 21 |
+
"kind": "POSITIONAL_OR_KEYWORD",
|
| 22 |
+
"default": null,
|
| 23 |
+
"annotation": "int"
|
| 24 |
+
},
|
| 25 |
+
{
|
| 26 |
+
"name": "softmax",
|
| 27 |
+
"kind": "POSITIONAL_OR_KEYWORD",
|
| 28 |
+
"default": "False",
|
| 29 |
+
"annotation": "bool"
|
| 30 |
+
}
|
| 31 |
+
],
|
| 32 |
+
"return_annotation": null
|
| 33 |
+
}
|
| 34 |
+
}
|
| 35 |
+
],
|
| 36 |
+
"layers": []
|
| 37 |
+
}
|
build/torch214-metal-aarch64-darwin/__init__.py
ADDED
|
@@ -0,0 +1,40 @@
|
|
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|
|
|
|
|
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|
|
|
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|
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|
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|
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|
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|
|
|
|
|
|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""Top-k over a small row, as a torch op.
|
| 2 |
+
|
| 3 |
+
For a MoE router. torch's MPS `topk` is a full sort, and so is ggml's -- `GGML_OP_TOP_K` dispatches
|
| 4 |
+
`kernel_argsort_f32_i32_desc` -- which is why this is written rather than ported: selecting the
|
| 5 |
+
largest 8 of 256 logits does not need the row ordered. 26us against 71us, once per layer per token.
|
| 6 |
+
"""
|
| 7 |
+
|
| 8 |
+
import os
|
| 9 |
+
from pathlib import Path
|
| 10 |
+
|
| 11 |
+
import torch
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
# A local (non-nix) build leaves the metallib on disk beside this module instead of embedding it, and
|
| 15 |
+
# `dispatch.mm` looks it up through this variable when nothing is embedded. Set before the extension
|
| 16 |
+
# is imported, and never over an existing value, so an explicit choice still wins.
|
| 17 |
+
_METALLIB = Path(__file__).parent / "ggml-metal.metallib"
|
| 18 |
+
if _METALLIB.is_file():
|
| 19 |
+
os.environ.setdefault("TOPK_METALLIB", str(_METALLIB))
|
| 20 |
+
|
| 21 |
+
from ._ops import add_op_namespace_prefix, ops # noqa: E402
|
| 22 |
+
from .layers import SoftmaxTopKRouter # noqa: E402,F401
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
def top_k(logits: torch.Tensor, k: int, softmax: bool = False):
|
| 26 |
+
"""`(values, indices)` of the largest `k` per row of a 2D f32 tensor.
|
| 27 |
+
|
| 28 |
+
`indices` is int32, which is what an expert-routed matmul wants, so routing them onward costs no
|
| 29 |
+
cast. With `softmax`, `values` is softmaxed over the selected k.
|
| 30 |
+
"""
|
| 31 |
+
return ops.top_k(logits, k, softmax)
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
@torch.library.register_fake(add_op_namespace_prefix("top_k"))
|
| 35 |
+
def _top_k_fake(logits, k, softmax):
|
| 36 |
+
rows = logits.shape[0]
|
| 37 |
+
return [logits.new_empty((rows, k)), logits.new_empty((rows, k), dtype=torch.int32)]
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
__all__ = ["SoftmaxTopKRouter", "top_k"]
|
build/torch214-metal-aarch64-darwin/_ops.py
ADDED
|
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import torch
|
| 2 |
+
from . import _topk_metal_fe2bb66
|
| 3 |
+
ops = torch.ops._topk_metal_fe2bb66
|
| 4 |
+
|
| 5 |
+
def add_op_namespace_prefix(op_name: str):
|
| 6 |
+
"""
|
| 7 |
+
Prefix op by namespace.
|
| 8 |
+
"""
|
| 9 |
+
return f"_topk_metal_fe2bb66::{op_name}"
|
build/torch214-metal-aarch64-darwin/_topk_metal_fe2bb66.abi3.so
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f4d45978cc77608da1dd50a3dd1a799c9be7b163cf4a5452691767e476bf1ef3
|
| 3 |
+
size 131288
|
build/torch214-metal-aarch64-darwin/layers.py
ADDED
|
@@ -0,0 +1,39 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""Layer-level entry points, for `kernels`' layer mapping.
|
| 2 |
+
|
| 3 |
+
`transformers` marks the layers a kernel may replace with `@use_kernel_forward_from_hub("<name>")`, and a
|
| 4 |
+
kernel repository supplies a class of that name whose `forward` is grafted onto the real module. The module
|
| 5 |
+
keeps its own parameters and constructor -- the class here is stateless, which `kernels` enforces: no
|
| 6 |
+
`__init__`, no members besides `forward`, and a signature matching the layer being replaced.
|
| 7 |
+
|
| 8 |
+
Named `SoftmaxTopKRouter` rather than `TopKRouter` because the rewrite below is only valid for routers
|
| 9 |
+
that score with a softmax. One that scores with a sigmoid returns the same shapes and would graft
|
| 10 |
+
cleanly onto this, then be quietly wrong.
|
| 11 |
+
"""
|
| 12 |
+
|
| 13 |
+
import torch
|
| 14 |
+
import torch.nn as nn
|
| 15 |
+
import torch.nn.functional as F
|
| 16 |
+
|
| 17 |
+
from ._ops import ops
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
class SoftmaxTopKRouter(nn.Module):
|
| 21 |
+
"""A softmax router's top-k, in two dispatches instead of four.
|
| 22 |
+
|
| 23 |
+
The model softmaxes over every expert, takes the top `k`, then renormalizes those `k`. Softmax is
|
| 24 |
+
monotonic, so the top `k` probabilities belong to the top `k` logits, and renormalizing them is a
|
| 25 |
+
softmax over just those logits -- `exp(x_i) / sum_{j in topk} exp(x_j)` either way. So the whole
|
| 26 |
+
tail is skipped: select first, then softmax the `k` that survived, which the kernel does in the
|
| 27 |
+
same pass.
|
| 28 |
+
|
| 29 |
+
Worth it because a MoE decode step is dispatch-bound rather than arithmetic-bound: this is 40
|
| 30 |
+
layers x 2 fewer operations, and the top-k itself stops being a full sort.
|
| 31 |
+
|
| 32 |
+
`indices` comes back int32, which is what an expert-routed matmul wants.
|
| 33 |
+
"""
|
| 34 |
+
|
| 35 |
+
def forward(self, hidden_states: torch.Tensor):
|
| 36 |
+
hidden_states = hidden_states.reshape(-1, self.hidden_dim)
|
| 37 |
+
router_logits = F.linear(hidden_states, self.weight)
|
| 38 |
+
scores, indices = ops.top_k(router_logits, self.top_k, True)
|
| 39 |
+
return router_logits, scores.to(router_logits.dtype), indices
|
build/torch214-metal-aarch64-darwin/metadata.json
ADDED
|
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"name": "topk",
|
| 3 |
+
"id": "_topk_metal_fe2bb66",
|
| 4 |
+
"version": 1,
|
| 5 |
+
"kernels-minver": "0.14.0",
|
| 6 |
+
"license": "MIT",
|
| 7 |
+
"python-depends": [],
|
| 8 |
+
"kernel-depends": [],
|
| 9 |
+
"backend": {
|
| 10 |
+
"type": "metal"
|
| 11 |
+
},
|
| 12 |
+
"digest": {
|
| 13 |
+
"algorithm": "sha256",
|
| 14 |
+
"files": {
|
| 15 |
+
"__init__.py": "cm0ZJnxWpFzy3M3RrxrS6q9T1otdIBa+Npd7kteAZbg=",
|
| 16 |
+
"_ops.py": "r7EEKA36ZAicnt9YXZ1CZ8k5OkzXbWUk3oowDZI1L6A=",
|
| 17 |
+
"_topk_metal_fe2bb66.abi3.so": "9NRZeMx3YI2h3VCj3Rp5nJvnsWPPSlRSaRdn5Ha/HvM=",
|
| 18 |
+
"layers.py": "DP5wFLC5D7eeoDBuFY9Eoi36CPOjreKcQJk1AF+eRHg=",
|
| 19 |
+
"symbols.json": "gLRlxKtWt92J4oVk9FQdvL89CipliDHfkh6yTVY1WZ0="
|
| 20 |
+
}
|
| 21 |
+
},
|
| 22 |
+
"provenance": {
|
| 23 |
+
"kernel-builder": {
|
| 24 |
+
"version": "0.18.0-dev0",
|
| 25 |
+
"commit": "d054906a13710b811b96697b4635f03020842733",
|
| 26 |
+
"dirty": false
|
| 27 |
+
},
|
| 28 |
+
"kernel": {
|
| 29 |
+
"commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
|
| 30 |
+
"dirty": false
|
| 31 |
+
}
|
| 32 |
+
}
|
| 33 |
+
}
|
build/torch214-metal-aarch64-darwin/metadata.json.sigstore
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
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|
build/torch214-metal-aarch64-darwin/symbols.json
ADDED
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@@ -0,0 +1,37 @@
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+
{
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| 2 |
+
"schema_version": 1,
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| 3 |
+
"module": "_topk_metal_fe2bb66",
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| 4 |
+
"functions": [
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| 5 |
+
{
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| 6 |
+
"name": "top_k",
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| 7 |
+
"qualname": "top_k",
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| 8 |
+
"module": "_topk_metal_fe2bb66",
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| 9 |
+
"kind": "function",
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| 10 |
+
"doc": "`(values, indices)` of the largest `k` per row of a 2D f32 tensor.\n\n`indices` is int32, which is what an expert-routed matmul wants, so routing them onward costs no\ncast. With `softmax`, `values` is softmaxed over the selected k.",
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| 11 |
+
"signature": {
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| 12 |
+
"parameters": [
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| 13 |
+
{
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| 14 |
+
"name": "logits",
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| 15 |
+
"kind": "POSITIONAL_OR_KEYWORD",
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| 16 |
+
"default": null,
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| 17 |
+
"annotation": "torch.Tensor"
|
| 18 |
+
},
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| 19 |
+
{
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| 20 |
+
"name": "k",
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| 21 |
+
"kind": "POSITIONAL_OR_KEYWORD",
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| 22 |
+
"default": null,
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| 23 |
+
"annotation": "int"
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| 24 |
+
},
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| 25 |
+
{
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| 26 |
+
"name": "softmax",
|
| 27 |
+
"kind": "POSITIONAL_OR_KEYWORD",
|
| 28 |
+
"default": "False",
|
| 29 |
+
"annotation": "bool"
|
| 30 |
+
}
|
| 31 |
+
],
|
| 32 |
+
"return_annotation": null
|
| 33 |
+
}
|
| 34 |
+
}
|
| 35 |
+
],
|
| 36 |
+
"layers": []
|
| 37 |
+
}
|
build/torch215-metal-aarch64-darwin/__init__.py
ADDED
|
@@ -0,0 +1,40 @@
|
|
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|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""Top-k over a small row, as a torch op.
|
| 2 |
+
|
| 3 |
+
For a MoE router. torch's MPS `topk` is a full sort, and so is ggml's -- `GGML_OP_TOP_K` dispatches
|
| 4 |
+
`kernel_argsort_f32_i32_desc` -- which is why this is written rather than ported: selecting the
|
| 5 |
+
largest 8 of 256 logits does not need the row ordered. 26us against 71us, once per layer per token.
|
| 6 |
+
"""
|
| 7 |
+
|
| 8 |
+
import os
|
| 9 |
+
from pathlib import Path
|
| 10 |
+
|
| 11 |
+
import torch
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
# A local (non-nix) build leaves the metallib on disk beside this module instead of embedding it, and
|
| 15 |
+
# `dispatch.mm` looks it up through this variable when nothing is embedded. Set before the extension
|
| 16 |
+
# is imported, and never over an existing value, so an explicit choice still wins.
|
| 17 |
+
_METALLIB = Path(__file__).parent / "ggml-metal.metallib"
|
| 18 |
+
if _METALLIB.is_file():
|
| 19 |
+
os.environ.setdefault("TOPK_METALLIB", str(_METALLIB))
|
| 20 |
+
|
| 21 |
+
from ._ops import add_op_namespace_prefix, ops # noqa: E402
|
| 22 |
+
from .layers import SoftmaxTopKRouter # noqa: E402,F401
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
def top_k(logits: torch.Tensor, k: int, softmax: bool = False):
|
| 26 |
+
"""`(values, indices)` of the largest `k` per row of a 2D f32 tensor.
|
| 27 |
+
|
| 28 |
+
`indices` is int32, which is what an expert-routed matmul wants, so routing them onward costs no
|
| 29 |
+
cast. With `softmax`, `values` is softmaxed over the selected k.
|
| 30 |
+
"""
|
| 31 |
+
return ops.top_k(logits, k, softmax)
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
@torch.library.register_fake(add_op_namespace_prefix("top_k"))
|
| 35 |
+
def _top_k_fake(logits, k, softmax):
|
| 36 |
+
rows = logits.shape[0]
|
| 37 |
+
return [logits.new_empty((rows, k)), logits.new_empty((rows, k), dtype=torch.int32)]
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
__all__ = ["SoftmaxTopKRouter", "top_k"]
|
build/torch215-metal-aarch64-darwin/_ops.py
ADDED
|
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import torch
|
| 2 |
+
from . import _topk_metal_fe2bb66
|
| 3 |
+
ops = torch.ops._topk_metal_fe2bb66
|
| 4 |
+
|
| 5 |
+
def add_op_namespace_prefix(op_name: str):
|
| 6 |
+
"""
|
| 7 |
+
Prefix op by namespace.
|
| 8 |
+
"""
|
| 9 |
+
return f"_topk_metal_fe2bb66::{op_name}"
|
build/torch215-metal-aarch64-darwin/_topk_metal_fe2bb66.abi3.so
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4d905be0262d63215edcff84567924e50aa861f9c9e1bc5d89dab638a94d8125
|
| 3 |
+
size 131288
|
build/torch215-metal-aarch64-darwin/layers.py
ADDED
|
@@ -0,0 +1,39 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""Layer-level entry points, for `kernels`' layer mapping.
|
| 2 |
+
|
| 3 |
+
`transformers` marks the layers a kernel may replace with `@use_kernel_forward_from_hub("<name>")`, and a
|
| 4 |
+
kernel repository supplies a class of that name whose `forward` is grafted onto the real module. The module
|
| 5 |
+
keeps its own parameters and constructor -- the class here is stateless, which `kernels` enforces: no
|
| 6 |
+
`__init__`, no members besides `forward`, and a signature matching the layer being replaced.
|
| 7 |
+
|
| 8 |
+
Named `SoftmaxTopKRouter` rather than `TopKRouter` because the rewrite below is only valid for routers
|
| 9 |
+
that score with a softmax. One that scores with a sigmoid returns the same shapes and would graft
|
| 10 |
+
cleanly onto this, then be quietly wrong.
|
| 11 |
+
"""
|
| 12 |
+
|
| 13 |
+
import torch
|
| 14 |
+
import torch.nn as nn
|
| 15 |
+
import torch.nn.functional as F
|
| 16 |
+
|
| 17 |
+
from ._ops import ops
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
class SoftmaxTopKRouter(nn.Module):
|
| 21 |
+
"""A softmax router's top-k, in two dispatches instead of four.
|
| 22 |
+
|
| 23 |
+
The model softmaxes over every expert, takes the top `k`, then renormalizes those `k`. Softmax is
|
| 24 |
+
monotonic, so the top `k` probabilities belong to the top `k` logits, and renormalizing them is a
|
| 25 |
+
softmax over just those logits -- `exp(x_i) / sum_{j in topk} exp(x_j)` either way. So the whole
|
| 26 |
+
tail is skipped: select first, then softmax the `k` that survived, which the kernel does in the
|
| 27 |
+
same pass.
|
| 28 |
+
|
| 29 |
+
Worth it because a MoE decode step is dispatch-bound rather than arithmetic-bound: this is 40
|
| 30 |
+
layers x 2 fewer operations, and the top-k itself stops being a full sort.
|
| 31 |
+
|
| 32 |
+
`indices` comes back int32, which is what an expert-routed matmul wants.
|
| 33 |
+
"""
|
| 34 |
+
|
| 35 |
+
def forward(self, hidden_states: torch.Tensor):
|
| 36 |
+
hidden_states = hidden_states.reshape(-1, self.hidden_dim)
|
| 37 |
+
router_logits = F.linear(hidden_states, self.weight)
|
| 38 |
+
scores, indices = ops.top_k(router_logits, self.top_k, True)
|
| 39 |
+
return router_logits, scores.to(router_logits.dtype), indices
|
build/torch215-metal-aarch64-darwin/metadata.json
ADDED
|
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"name": "topk",
|
| 3 |
+
"id": "_topk_metal_fe2bb66",
|
| 4 |
+
"version": 1,
|
| 5 |
+
"kernels-minver": "0.14.0",
|
| 6 |
+
"license": "MIT",
|
| 7 |
+
"python-depends": [],
|
| 8 |
+
"kernel-depends": [],
|
| 9 |
+
"backend": {
|
| 10 |
+
"type": "metal"
|
| 11 |
+
},
|
| 12 |
+
"digest": {
|
| 13 |
+
"algorithm": "sha256",
|
| 14 |
+
"files": {
|
| 15 |
+
"__init__.py": "cm0ZJnxWpFzy3M3RrxrS6q9T1otdIBa+Npd7kteAZbg=",
|
| 16 |
+
"_ops.py": "r7EEKA36ZAicnt9YXZ1CZ8k5OkzXbWUk3oowDZI1L6A=",
|
| 17 |
+
"_topk_metal_fe2bb66.abi3.so": "TZBb4CYtYyFe3P+EVnkk5QqoYfnJ4bxdidq2OKlNgSU=",
|
| 18 |
+
"layers.py": "DP5wFLC5D7eeoDBuFY9Eoi36CPOjreKcQJk1AF+eRHg=",
|
| 19 |
+
"symbols.json": "gLRlxKtWt92J4oVk9FQdvL89CipliDHfkh6yTVY1WZ0="
|
| 20 |
+
}
|
| 21 |
+
},
|
| 22 |
+
"provenance": {
|
| 23 |
+
"kernel-builder": {
|
| 24 |
+
"version": "0.18.0-dev0",
|
| 25 |
+
"commit": "d054906a13710b811b96697b4635f03020842733",
|
| 26 |
+
"dirty": false
|
| 27 |
+
},
|
| 28 |
+
"kernel": {
|
| 29 |
+
"commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
|
| 30 |
+
"dirty": false
|
| 31 |
+
}
|
| 32 |
+
}
|
| 33 |
+
}
|
build/torch215-metal-aarch64-darwin/metadata.json.sigstore
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
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|
build/torch215-metal-aarch64-darwin/symbols.json
ADDED
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@@ -0,0 +1,37 @@
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+
{
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| 2 |
+
"schema_version": 1,
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| 3 |
+
"module": "_topk_metal_fe2bb66",
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| 4 |
+
"functions": [
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| 5 |
+
{
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| 6 |
+
"name": "top_k",
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| 7 |
+
"qualname": "top_k",
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| 8 |
+
"module": "_topk_metal_fe2bb66",
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| 9 |
+
"kind": "function",
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| 10 |
+
"doc": "`(values, indices)` of the largest `k` per row of a 2D f32 tensor.\n\n`indices` is int32, which is what an expert-routed matmul wants, so routing them onward costs no\ncast. With `softmax`, `values` is softmaxed over the selected k.",
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| 11 |
+
"signature": {
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| 12 |
+
"parameters": [
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| 13 |
+
{
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| 14 |
+
"name": "logits",
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| 15 |
+
"kind": "POSITIONAL_OR_KEYWORD",
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| 16 |
+
"default": null,
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| 17 |
+
"annotation": "torch.Tensor"
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| 18 |
+
},
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| 19 |
+
{
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| 20 |
+
"name": "k",
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| 21 |
+
"kind": "POSITIONAL_OR_KEYWORD",
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| 22 |
+
"default": null,
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| 23 |
+
"annotation": "int"
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| 24 |
+
},
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| 25 |
+
{
|
| 26 |
+
"name": "softmax",
|
| 27 |
+
"kind": "POSITIONAL_OR_KEYWORD",
|
| 28 |
+
"default": "False",
|
| 29 |
+
"annotation": "bool"
|
| 30 |
+
}
|
| 31 |
+
],
|
| 32 |
+
"return_annotation": null
|
| 33 |
+
}
|
| 34 |
+
}
|
| 35 |
+
],
|
| 36 |
+
"layers": []
|
| 37 |
+
}
|