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README.md +30 -0
build/torch210-metal-aarch64-darwin/__init__.py +98 -0
build/torch210-metal-aarch64-darwin/_ggml_quantization_metal_fe2bb66.abi3.so +3 -0
build/torch210-metal-aarch64-darwin/_ops.py +9 -0
build/torch210-metal-aarch64-darwin/metadata.json +32 -0
build/torch210-metal-aarch64-darwin/metadata.json.sigstore +1 -0
build/torch210-metal-aarch64-darwin/symbols.json +169 -0
build/torch211-metal-aarch64-darwin/__init__.py +98 -0
build/torch211-metal-aarch64-darwin/_ggml_quantization_metal_fe2bb66.abi3.so +3 -0
build/torch211-metal-aarch64-darwin/_ops.py +9 -0
build/torch211-metal-aarch64-darwin/metadata.json +32 -0
build/torch211-metal-aarch64-darwin/metadata.json.sigstore +1 -0
build/torch211-metal-aarch64-darwin/symbols.json +169 -0
build/torch212-metal-aarch64-darwin/{_ggml_quantization_metal_63f3c1e.abi3.so → _ggml_quantization_metal_fe2bb66.abi3.so} +1 -1
build/torch212-metal-aarch64-darwin/_ops.py +3 -3
build/torch212-metal-aarch64-darwin/metadata.json +9 -6
build/torch212-metal-aarch64-darwin/metadata.json.sigstore +1 -1
build/torch212-metal-aarch64-darwin/symbols.json +169 -0
build/torch213-metal-aarch64-darwin/{_ggml_quantization_metal_63f3c1e.abi3.so → _ggml_quantization_metal_fe2bb66.abi3.so} +1 -1
build/torch213-metal-aarch64-darwin/_ops.py +3 -3
build/torch213-metal-aarch64-darwin/metadata.json +9 -6
build/torch213-metal-aarch64-darwin/metadata.json.sigstore +1 -1
build/torch213-metal-aarch64-darwin/symbols.json +169 -0
build/torch214-metal-aarch64-darwin/__init__.py +98 -0
build/torch214-metal-aarch64-darwin/_ggml_quantization_metal_fe2bb66.abi3.so +3 -0
build/torch214-metal-aarch64-darwin/_ops.py +9 -0
build/torch214-metal-aarch64-darwin/metadata.json +32 -0
build/torch214-metal-aarch64-darwin/metadata.json.sigstore +1 -0
build/torch214-metal-aarch64-darwin/symbols.json +169 -0
build/torch215-metal-aarch64-darwin/__init__.py +98 -0
build/torch215-metal-aarch64-darwin/_ggml_quantization_metal_fe2bb66.abi3.so +3 -0
build/torch215-metal-aarch64-darwin/_ops.py +9 -0
build/torch215-metal-aarch64-darwin/metadata.json +32 -0
build/torch215-metal-aarch64-darwin/metadata.json.sigstore +1 -0
build/torch215-metal-aarch64-darwin/symbols.json +169 -0

README.md ADDED Viewed

	@@ -0,0 +1,30 @@

+---
+library_name: kernels
+license: mit
+---
+This is the repository card of ggml-org/ggml-quantization 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-quantization", version=1)
+GEMV_TYPES = kernel_module.GEMV_TYPES
+GEMV_TYPES(...)
+```
+## Available functions
+- `GEMV_TYPES`
+- `MAX_GEMV_ROWS`
+- `dequantize`
+- `get_rows`
+- `mul_mat_id`
+- `mul_mat_vec`
+## Benchmarks
+No benchmark available yet.

build/torch210-metal-aarch64-darwin/__init__.py ADDED Viewed

	@@ -0,0 +1,98 @@

+"""Compute directly on the packed blocks of a GGUF checkpoint.
+A GGUF weight is stored as blocks of 32 or 256 values sharing a scale. These kernels read that
+layout as it is, so a quantized model can be loaded and run without ever materializing a dense
+copy — which is the whole memory saving.
+Ported from llama.cpp's ggml-cuda; `vendor/UPSTREAM` pins the revision. The ops are named for what
+they do rather than for a backend: each backend registers its own implementation of the same
+schema, so calls dispatch on the tensor's device.
+"""
+import torch
+from ._ops import add_op_namespace_prefix, ops
+__all__ = ["GEMV_TYPES", "MAX_GEMV_ROWS", "dequantize", "get_rows", "mul_mat_id", "mul_mat_vec"]
+# Upstream's MMVQ_MAX_BATCH_SIZE: `mul_mat_vec` has no implementation beyond this many rows, so a
+# caller with more (prefill) dequantizes and uses an ordinary matmul.
+MAX_GEMV_ROWS = 8
+# ggml type ids `mul_mat_vec` implements, as this build reports them -- it is backend-specific, and
+# a type routed into a gemv that has no kernel for it is a fault, not a fallback. CUDA covers
+# Q4_0/Q4_1/Q5_0/Q5_1/Q8_0, the K quants, the IQ quants, MXFP4/NVFP4 and Q1_0/Q2_0; Metal is the
+# same minus NVFP4/Q1_0/Q2_0, which its metallib has no kernels for. `dequantize` covers more, so
+# check this before choosing the fused path.
+try:
+    GEMV_TYPES = frozenset(ops.gemv_types())
+except AttributeError:
+    # A build published before `gemv_types` existed; those are CUDA-only, so its list is theirs.
+    GEMV_TYPES = frozenset({2, 3, 6, 7, 8, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 29, 39, 40, 41, 42})
+def get_rows(
+    blocks: torch.Tensor, indices: torch.Tensor, ggml_type: int, cols: int, dtype: torch.dtype
+) -> torch.Tensor:
+    """The rows `indices` names, unpacked: `(rows, bytes_per_row)` uint8 -> `(len(indices), cols)`.
+    ggml's `get_rows`, which dequantizes as it gathers -- so reading a few rows out of a large table
+    never touches the rest.
+    """
+    return ops.get_rows(blocks, indices, ggml_type, cols, dtype)
+def dequantize(
+    blocks: torch.Tensor, ggml_type: int, rows: int, cols: int, dtype: torch.dtype
+) -> torch.Tensor:
+    """`(rows, bytes_per_row)` uint8 blocks -> `(rows, cols)` values of `dtype`."""
+    return ops.dequantize(blocks, ggml_type, rows, cols, dtype)
+def mul_mat_vec(
+    blocks: torch.Tensor, x: torch.Tensor, ggml_type: int, out_features: int
+) -> torch.Tensor:
+    """Fused dequantize-gemv: `x @ blocks.T` without unpacking `blocks`.
+    `x` is `(rows, in_features)` and `rows` must be at most `MAX_GEMV_ROWS`. The result is f32
+    whatever `x`'s dtype was, since the kernel writes an f32 destination; cast it at the call site,
+    where the cast can be fused into whatever consumes it.
+    """
+    return ops.mul_mat_vec(blocks, x, ggml_type, out_features)
+def mul_mat_id(
+    blocks: torch.Tensor, x: torch.Tensor, ids: torch.Tensor, ggml_type: int, out_features: int
+) -> torch.Tensor:
+    """One dispatch for a bank of routed experts: ggml's `mul_mv_id`.
+    `blocks` is `(n_experts, out_features, bytes_per_row)`, `x` is `(n_tokens, in_features)`, and
+    `ids` is `(n_tokens, n_used)` naming the expert each of a token's slots picked. The result is
+    `(n_tokens, n_used, out_features)` f32, one row per slot.
+    The alternative is a gemv per expert per layer, whose arithmetic is dwarfed by the dispatch
+    around it -- which is most of what a MoE decode step costs.
+    """
+    return ops.mul_mat_id(blocks, x, ids, ggml_type, out_features)
+# Without these, torch.compile cannot trace the ops and breaks the graph at every call.
+@torch.library.register_fake(add_op_namespace_prefix("get_rows"))
+def _get_rows_fake(blocks, indices, ggml_type, cols, dtype):
+    return blocks.new_empty((indices.numel(), cols), dtype=dtype)
+@torch.library.register_fake(add_op_namespace_prefix("dequantize"))
+def _dequantize_fake(blocks, ggml_type, rows, cols, dtype):
+    return blocks.new_empty((rows, cols), dtype=dtype)
+@torch.library.register_fake(add_op_namespace_prefix("mul_mat_vec"))
+def _mul_mat_vec_fake(blocks, x, ggml_type, out_features):
+    return x.new_empty((x.shape[0], out_features), dtype=torch.float32)
+@torch.library.register_fake(add_op_namespace_prefix("mul_mat_id"))
+def _mul_mat_id_fake(blocks, x, ids, ggml_type, out_features):
+    return x.new_empty((x.shape[0], ids.shape[1], out_features), dtype=torch.float32)

build/torch210-metal-aarch64-darwin/_ggml_quantization_metal_fe2bb66.abi3.so ADDED Viewed

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build/torch210-metal-aarch64-darwin/_ops.py ADDED Viewed

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

+import torch
+from . import _ggml_quantization_metal_fe2bb66
+ops = torch.ops._ggml_quantization_metal_fe2bb66
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_ggml_quantization_metal_fe2bb66::{op_name}"

build/torch210-metal-aarch64-darwin/metadata.json ADDED Viewed

	@@ -0,0 +1,32 @@

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+  "license": "MIT",
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+  },
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+    }
+  },
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+    }
+  }
+}

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build/torch210-metal-aarch64-darwin/symbols.json ADDED Viewed

	@@ -0,0 +1,169 @@

+{
+  "schema_version": 1,
+  "module": "_ggml_quantization_metal_fe2bb66",
+  "functions": [
+    {
+      "name": "dequantize",
+      "qualname": "dequantize",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "`(rows, bytes_per_row)` uint8 blocks -> `(rows, cols)` values of `dtype`.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "rows",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "cols",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "dtype",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.dtype"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    },
+    {
+      "name": "get_rows",
+      "qualname": "get_rows",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "The rows `indices` names, unpacked: `(rows, bytes_per_row)` uint8 -> `(len(indices), cols)`.\n\nggml's `get_rows`, which dequantizes as it gathers -- so reading a few rows out of a large table\nnever touches the rest.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "indices",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "cols",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "dtype",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.dtype"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    },
+    {
+      "name": "mul_mat_id",
+      "qualname": "mul_mat_id",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "One dispatch for a bank of routed experts: ggml's `mul_mv_id`.\n\n`blocks` is `(n_experts, out_features, bytes_per_row)`, `x` is `(n_tokens, in_features)`, and\n`ids` is `(n_tokens, n_used)` naming the expert each of a token's slots picked. The result is\n`(n_tokens, n_used, out_features)` f32, one row per slot.\n\nThe alternative is a gemv per expert per layer, whose arithmetic is dwarfed by the dispatch\naround it -- which is most of what a MoE decode step costs.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "x",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ids",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "out_features",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    },
+    {
+      "name": "mul_mat_vec",
+      "qualname": "mul_mat_vec",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "Fused dequantize-gemv: `x @ blocks.T` without unpacking `blocks`.\n\n`x` is `(rows, in_features)` and `rows` must be at most `MAX_GEMV_ROWS`. The result is f32\nwhatever `x`'s dtype was, since the kernel writes an f32 destination; cast it at the call site,\nwhere the cast can be fused into whatever consumes it.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "x",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "out_features",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    }
+  ],
+  "layers": []
+}

build/torch211-metal-aarch64-darwin/__init__.py ADDED Viewed

	@@ -0,0 +1,98 @@

+"""Compute directly on the packed blocks of a GGUF checkpoint.
+A GGUF weight is stored as blocks of 32 or 256 values sharing a scale. These kernels read that
+layout as it is, so a quantized model can be loaded and run without ever materializing a dense
+copy — which is the whole memory saving.
+Ported from llama.cpp's ggml-cuda; `vendor/UPSTREAM` pins the revision. The ops are named for what
+they do rather than for a backend: each backend registers its own implementation of the same
+schema, so calls dispatch on the tensor's device.
+"""
+import torch
+from ._ops import add_op_namespace_prefix, ops
+__all__ = ["GEMV_TYPES", "MAX_GEMV_ROWS", "dequantize", "get_rows", "mul_mat_id", "mul_mat_vec"]
+# Upstream's MMVQ_MAX_BATCH_SIZE: `mul_mat_vec` has no implementation beyond this many rows, so a
+# caller with more (prefill) dequantizes and uses an ordinary matmul.
+MAX_GEMV_ROWS = 8
+# ggml type ids `mul_mat_vec` implements, as this build reports them -- it is backend-specific, and
+# a type routed into a gemv that has no kernel for it is a fault, not a fallback. CUDA covers
+# Q4_0/Q4_1/Q5_0/Q5_1/Q8_0, the K quants, the IQ quants, MXFP4/NVFP4 and Q1_0/Q2_0; Metal is the
+# same minus NVFP4/Q1_0/Q2_0, which its metallib has no kernels for. `dequantize` covers more, so
+# check this before choosing the fused path.
+try:
+    GEMV_TYPES = frozenset(ops.gemv_types())
+except AttributeError:
+    # A build published before `gemv_types` existed; those are CUDA-only, so its list is theirs.
+    GEMV_TYPES = frozenset({2, 3, 6, 7, 8, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 29, 39, 40, 41, 42})
+def get_rows(
+    blocks: torch.Tensor, indices: torch.Tensor, ggml_type: int, cols: int, dtype: torch.dtype
+) -> torch.Tensor:
+    """The rows `indices` names, unpacked: `(rows, bytes_per_row)` uint8 -> `(len(indices), cols)`.
+    ggml's `get_rows`, which dequantizes as it gathers -- so reading a few rows out of a large table
+    never touches the rest.
+    """
+    return ops.get_rows(blocks, indices, ggml_type, cols, dtype)
+def dequantize(
+    blocks: torch.Tensor, ggml_type: int, rows: int, cols: int, dtype: torch.dtype
+) -> torch.Tensor:
+    """`(rows, bytes_per_row)` uint8 blocks -> `(rows, cols)` values of `dtype`."""
+    return ops.dequantize(blocks, ggml_type, rows, cols, dtype)
+def mul_mat_vec(
+    blocks: torch.Tensor, x: torch.Tensor, ggml_type: int, out_features: int
+) -> torch.Tensor:
+    """Fused dequantize-gemv: `x @ blocks.T` without unpacking `blocks`.
+    `x` is `(rows, in_features)` and `rows` must be at most `MAX_GEMV_ROWS`. The result is f32
+    whatever `x`'s dtype was, since the kernel writes an f32 destination; cast it at the call site,
+    where the cast can be fused into whatever consumes it.
+    """
+    return ops.mul_mat_vec(blocks, x, ggml_type, out_features)
+def mul_mat_id(
+    blocks: torch.Tensor, x: torch.Tensor, ids: torch.Tensor, ggml_type: int, out_features: int
+) -> torch.Tensor:
+    """One dispatch for a bank of routed experts: ggml's `mul_mv_id`.
+    `blocks` is `(n_experts, out_features, bytes_per_row)`, `x` is `(n_tokens, in_features)`, and
+    `ids` is `(n_tokens, n_used)` naming the expert each of a token's slots picked. The result is
+    `(n_tokens, n_used, out_features)` f32, one row per slot.
+    The alternative is a gemv per expert per layer, whose arithmetic is dwarfed by the dispatch
+    around it -- which is most of what a MoE decode step costs.
+    """
+    return ops.mul_mat_id(blocks, x, ids, ggml_type, out_features)
+# Without these, torch.compile cannot trace the ops and breaks the graph at every call.
+@torch.library.register_fake(add_op_namespace_prefix("get_rows"))
+def _get_rows_fake(blocks, indices, ggml_type, cols, dtype):
+    return blocks.new_empty((indices.numel(), cols), dtype=dtype)
+@torch.library.register_fake(add_op_namespace_prefix("dequantize"))
+def _dequantize_fake(blocks, ggml_type, rows, cols, dtype):
+    return blocks.new_empty((rows, cols), dtype=dtype)
+@torch.library.register_fake(add_op_namespace_prefix("mul_mat_vec"))
+def _mul_mat_vec_fake(blocks, x, ggml_type, out_features):
+    return x.new_empty((x.shape[0], out_features), dtype=torch.float32)
+@torch.library.register_fake(add_op_namespace_prefix("mul_mat_id"))
+def _mul_mat_id_fake(blocks, x, ids, ggml_type, out_features):
+    return x.new_empty((x.shape[0], ids.shape[1], out_features), dtype=torch.float32)

build/torch211-metal-aarch64-darwin/_ggml_quantization_metal_fe2bb66.abi3.so ADDED Viewed

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

+version https://git-lfs.github.com/spec/v1
+oid sha256:c34eb8ea993475e3665a09c6912951c9d78c12ee30e719c0112609d7409fe9bc
+size 3347640

build/torch211-metal-aarch64-darwin/_ops.py ADDED Viewed

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

+import torch
+from . import _ggml_quantization_metal_fe2bb66
+ops = torch.ops._ggml_quantization_metal_fe2bb66
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_ggml_quantization_metal_fe2bb66::{op_name}"

build/torch211-metal-aarch64-darwin/metadata.json ADDED Viewed

	@@ -0,0 +1,32 @@

+{
+  "name": "ggml-quantization",
+  "id": "_ggml_quantization_metal_fe2bb66",
+  "version": 1,
+  "kernels-minver": "0.14.0",
+  "license": "MIT",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "metal"
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
+      "__init__.py": "iPXgZXBgY3CzpNUSjmCovMVNPLBol1KBaNoTzkXp5mM=",
+      "_ggml_quantization_metal_fe2bb66.abi3.so": "w0646pk0deNmWgnGkSlRydeMEu4w5xnAESYJ10Cf6bw=",
+      "_ops.py": "7JPqKK8z3jmEj5qhydHkeSXOsZMXb424/1/5MR/BZuk=",
+      "symbols.json": "y6E2ftRiVpYGHN9aFeGUjoXci6YSNZRswRiLbHiaxFw="
+    }
+  },
+  "provenance": {
+    "kernel-builder": {
+      "version": "0.18.0-dev0",
+      "commit": "d054906a13710b811b96697b4635f03020842733",
+      "dirty": false
+    },
+    "kernel": {
+      "commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
+      "dirty": false
+    }
+  }
+}

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build/torch211-metal-aarch64-darwin/symbols.json ADDED Viewed

	@@ -0,0 +1,169 @@

+{
+  "schema_version": 1,
+  "module": "_ggml_quantization_metal_fe2bb66",
+  "functions": [
+    {
+      "name": "dequantize",
+      "qualname": "dequantize",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "`(rows, bytes_per_row)` uint8 blocks -> `(rows, cols)` values of `dtype`.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "rows",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "cols",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "dtype",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.dtype"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    },
+    {
+      "name": "get_rows",
+      "qualname": "get_rows",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "The rows `indices` names, unpacked: `(rows, bytes_per_row)` uint8 -> `(len(indices), cols)`.\n\nggml's `get_rows`, which dequantizes as it gathers -- so reading a few rows out of a large table\nnever touches the rest.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "indices",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
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+      "qualname": "mul_mat_id",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "One dispatch for a bank of routed experts: ggml's `mul_mv_id`.\n\n`blocks` is `(n_experts, out_features, bytes_per_row)`, `x` is `(n_tokens, in_features)`, and\n`ids` is `(n_tokens, n_used)` naming the expert each of a token's slots picked. The result is\n`(n_tokens, n_used, out_features)` f32, one row per slot.\n\nThe alternative is a gemv per expert per layer, whose arithmetic is dwarfed by the dispatch\naround it -- which is most of what a MoE decode step costs.",
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+      "qualname": "mul_mat_vec",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "Fused dequantize-gemv: `x @ blocks.T` without unpacking `blocks`.\n\n`x` is `(rows, in_features)` and `rows` must be at most `MAX_GEMV_ROWS`. The result is f32\nwhatever `x`'s dtype was, since the kernel writes an f32 destination; cast it at the call site,\nwhere the cast can be fused into whatever consumes it.",
+      "signature": {
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+          {
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+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
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+            "default": null,
+            "annotation": "torch.Tensor"
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+            "annotation": "int"
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+            "kind": "POSITIONAL_OR_KEYWORD",
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+            "annotation": "int"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    }
+  ],
+  "layers": []
+}

build/torch212-metal-aarch64-darwin/{_ggml_quantization_metal_63f3c1e.abi3.so → _ggml_quantization_metal_fe2bb66.abi3.so} RENAMED Viewed

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build/torch212-metal-aarch64-darwin/_ops.py CHANGED Viewed

@@ -1,9 +1,9 @@
 import torch
-from . import _ggml_quantization_metal_63f3c1e
-ops = torch.ops._ggml_quantization_metal_63f3c1e
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_ggml_quantization_metal_63f3c1e::{op_name}"

 import torch
+from . import _ggml_quantization_metal_fe2bb66
+ops = torch.ops._ggml_quantization_metal_fe2bb66
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
+    return f"_ggml_quantization_metal_fe2bb66::{op_name}"

build/torch212-metal-aarch64-darwin/metadata.json CHANGED Viewed

@@ -1,9 +1,11 @@
 {
   "name": "ggml-quantization",
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   "version": 1,
   "license": "MIT",
   "python-depends": [],
   "backend": {
     "type": "metal"
   },
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       "dirty": false
     }
   }

 {
   "name": "ggml-quantization",
+  "id": "_ggml_quantization_metal_fe2bb66",
   "version": 1,
+  "kernels-minver": "0.14.0",
   "license": "MIT",
   "python-depends": [],
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   "backend": {
     "type": "metal"
   },
     "algorithm": "sha256",
     "files": {
       "__init__.py": "iPXgZXBgY3CzpNUSjmCovMVNPLBol1KBaNoTzkXp5mM=",
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+      "_ops.py": "7JPqKK8z3jmEj5qhydHkeSXOsZMXb424/1/5MR/BZuk=",
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     }
   },
   "provenance": {
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       "dirty": false
     },
     "kernel": {
+      "commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
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build/torch212-metal-aarch64-darwin/symbols.json ADDED Viewed

	@@ -0,0 +1,169 @@

+{
+  "schema_version": 1,
+  "module": "_ggml_quantization_metal_fe2bb66",
+  "functions": [
+    {
+      "name": "dequantize",
+      "qualname": "dequantize",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "`(rows, bytes_per_row)` uint8 blocks -> `(rows, cols)` values of `dtype`.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "rows",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "cols",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "dtype",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.dtype"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    },
+    {
+      "name": "get_rows",
+      "qualname": "get_rows",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "The rows `indices` names, unpacked: `(rows, bytes_per_row)` uint8 -> `(len(indices), cols)`.\n\nggml's `get_rows`, which dequantizes as it gathers -- so reading a few rows out of a large table\nnever touches the rest.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "indices",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "cols",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "dtype",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.dtype"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    },
+    {
+      "name": "mul_mat_id",
+      "qualname": "mul_mat_id",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "One dispatch for a bank of routed experts: ggml's `mul_mv_id`.\n\n`blocks` is `(n_experts, out_features, bytes_per_row)`, `x` is `(n_tokens, in_features)`, and\n`ids` is `(n_tokens, n_used)` naming the expert each of a token's slots picked. The result is\n`(n_tokens, n_used, out_features)` f32, one row per slot.\n\nThe alternative is a gemv per expert per layer, whose arithmetic is dwarfed by the dispatch\naround it -- which is most of what a MoE decode step costs.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "x",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ids",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "out_features",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    },
+    {
+      "name": "mul_mat_vec",
+      "qualname": "mul_mat_vec",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "Fused dequantize-gemv: `x @ blocks.T` without unpacking `blocks`.\n\n`x` is `(rows, in_features)` and `rows` must be at most `MAX_GEMV_ROWS`. The result is f32\nwhatever `x`'s dtype was, since the kernel writes an f32 destination; cast it at the call site,\nwhere the cast can be fused into whatever consumes it.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "x",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "out_features",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    }
+  ],
+  "layers": []
+}

build/torch213-metal-aarch64-darwin/{_ggml_quantization_metal_63f3c1e.abi3.so → _ggml_quantization_metal_fe2bb66.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_quantization_metal_63f3c1e
-ops = torch.ops._ggml_quantization_metal_63f3c1e
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
-    return f"_ggml_quantization_metal_63f3c1e::{op_name}"

 import torch
+from . import _ggml_quantization_metal_fe2bb66
+ops = torch.ops._ggml_quantization_metal_fe2bb66
 def add_op_namespace_prefix(op_name: str):
     """
     Prefix op by namespace.
     """
+    return f"_ggml_quantization_metal_fe2bb66::{op_name}"

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

@@ -1,9 +1,11 @@
 {
   "name": "ggml-quantization",
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   "version": 1,
   "license": "MIT",
   "python-depends": [],
   "backend": {
     "type": "metal"
   },
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       "dirty": false
     }
   }

 {
   "name": "ggml-quantization",
+  "id": "_ggml_quantization_metal_fe2bb66",
   "version": 1,
+  "kernels-minver": "0.14.0",
   "license": "MIT",
   "python-depends": [],
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   "backend": {
     "type": "metal"
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     "algorithm": "sha256",
     "files": {
       "__init__.py": "iPXgZXBgY3CzpNUSjmCovMVNPLBol1KBaNoTzkXp5mM=",
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+      "_ops.py": "7JPqKK8z3jmEj5qhydHkeSXOsZMXb424/1/5MR/BZuk=",
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     }
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       "dirty": false
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build/torch213-metal-aarch64-darwin/symbols.json ADDED Viewed

	@@ -0,0 +1,169 @@

+{
+  "schema_version": 1,
+  "module": "_ggml_quantization_metal_fe2bb66",
+  "functions": [
+    {
+      "name": "dequantize",
+      "qualname": "dequantize",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "`(rows, bytes_per_row)` uint8 blocks -> `(rows, cols)` values of `dtype`.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "rows",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "cols",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "dtype",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.dtype"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    },
+    {
+      "name": "get_rows",
+      "qualname": "get_rows",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "The rows `indices` names, unpacked: `(rows, bytes_per_row)` uint8 -> `(len(indices), cols)`.\n\nggml's `get_rows`, which dequantizes as it gathers -- so reading a few rows out of a large table\nnever touches the rest.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "indices",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "cols",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "dtype",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.dtype"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    },
+    {
+      "name": "mul_mat_id",
+      "qualname": "mul_mat_id",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "One dispatch for a bank of routed experts: ggml's `mul_mv_id`.\n\n`blocks` is `(n_experts, out_features, bytes_per_row)`, `x` is `(n_tokens, in_features)`, and\n`ids` is `(n_tokens, n_used)` naming the expert each of a token's slots picked. The result is\n`(n_tokens, n_used, out_features)` f32, one row per slot.\n\nThe alternative is a gemv per expert per layer, whose arithmetic is dwarfed by the dispatch\naround it -- which is most of what a MoE decode step costs.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "x",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ids",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "out_features",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    },
+    {
+      "name": "mul_mat_vec",
+      "qualname": "mul_mat_vec",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "Fused dequantize-gemv: `x @ blocks.T` without unpacking `blocks`.\n\n`x` is `(rows, in_features)` and `rows` must be at most `MAX_GEMV_ROWS`. The result is f32\nwhatever `x`'s dtype was, since the kernel writes an f32 destination; cast it at the call site,\nwhere the cast can be fused into whatever consumes it.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "x",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "out_features",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    }
+  ],
+  "layers": []
+}

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

	@@ -0,0 +1,98 @@

+"""Compute directly on the packed blocks of a GGUF checkpoint.
+A GGUF weight is stored as blocks of 32 or 256 values sharing a scale. These kernels read that
+layout as it is, so a quantized model can be loaded and run without ever materializing a dense
+copy — which is the whole memory saving.
+Ported from llama.cpp's ggml-cuda; `vendor/UPSTREAM` pins the revision. The ops are named for what
+they do rather than for a backend: each backend registers its own implementation of the same
+schema, so calls dispatch on the tensor's device.
+"""
+import torch
+from ._ops import add_op_namespace_prefix, ops
+__all__ = ["GEMV_TYPES", "MAX_GEMV_ROWS", "dequantize", "get_rows", "mul_mat_id", "mul_mat_vec"]
+# Upstream's MMVQ_MAX_BATCH_SIZE: `mul_mat_vec` has no implementation beyond this many rows, so a
+# caller with more (prefill) dequantizes and uses an ordinary matmul.
+MAX_GEMV_ROWS = 8
+# ggml type ids `mul_mat_vec` implements, as this build reports them -- it is backend-specific, and
+# a type routed into a gemv that has no kernel for it is a fault, not a fallback. CUDA covers
+# Q4_0/Q4_1/Q5_0/Q5_1/Q8_0, the K quants, the IQ quants, MXFP4/NVFP4 and Q1_0/Q2_0; Metal is the
+# same minus NVFP4/Q1_0/Q2_0, which its metallib has no kernels for. `dequantize` covers more, so
+# check this before choosing the fused path.
+try:
+    GEMV_TYPES = frozenset(ops.gemv_types())
+except AttributeError:
+    # A build published before `gemv_types` existed; those are CUDA-only, so its list is theirs.
+    GEMV_TYPES = frozenset({2, 3, 6, 7, 8, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 29, 39, 40, 41, 42})
+def get_rows(
+    blocks: torch.Tensor, indices: torch.Tensor, ggml_type: int, cols: int, dtype: torch.dtype
+) -> torch.Tensor:
+    """The rows `indices` names, unpacked: `(rows, bytes_per_row)` uint8 -> `(len(indices), cols)`.
+    ggml's `get_rows`, which dequantizes as it gathers -- so reading a few rows out of a large table
+    never touches the rest.
+    """
+    return ops.get_rows(blocks, indices, ggml_type, cols, dtype)
+def dequantize(
+    blocks: torch.Tensor, ggml_type: int, rows: int, cols: int, dtype: torch.dtype
+) -> torch.Tensor:
+    """`(rows, bytes_per_row)` uint8 blocks -> `(rows, cols)` values of `dtype`."""
+    return ops.dequantize(blocks, ggml_type, rows, cols, dtype)
+def mul_mat_vec(
+    blocks: torch.Tensor, x: torch.Tensor, ggml_type: int, out_features: int
+) -> torch.Tensor:
+    """Fused dequantize-gemv: `x @ blocks.T` without unpacking `blocks`.
+    `x` is `(rows, in_features)` and `rows` must be at most `MAX_GEMV_ROWS`. The result is f32
+    whatever `x`'s dtype was, since the kernel writes an f32 destination; cast it at the call site,
+    where the cast can be fused into whatever consumes it.
+    """
+    return ops.mul_mat_vec(blocks, x, ggml_type, out_features)
+def mul_mat_id(
+    blocks: torch.Tensor, x: torch.Tensor, ids: torch.Tensor, ggml_type: int, out_features: int
+) -> torch.Tensor:
+    """One dispatch for a bank of routed experts: ggml's `mul_mv_id`.
+    `blocks` is `(n_experts, out_features, bytes_per_row)`, `x` is `(n_tokens, in_features)`, and
+    `ids` is `(n_tokens, n_used)` naming the expert each of a token's slots picked. The result is
+    `(n_tokens, n_used, out_features)` f32, one row per slot.
+    The alternative is a gemv per expert per layer, whose arithmetic is dwarfed by the dispatch
+    around it -- which is most of what a MoE decode step costs.
+    """
+    return ops.mul_mat_id(blocks, x, ids, ggml_type, out_features)
+# Without these, torch.compile cannot trace the ops and breaks the graph at every call.
+@torch.library.register_fake(add_op_namespace_prefix("get_rows"))
+def _get_rows_fake(blocks, indices, ggml_type, cols, dtype):
+    return blocks.new_empty((indices.numel(), cols), dtype=dtype)
+@torch.library.register_fake(add_op_namespace_prefix("dequantize"))
+def _dequantize_fake(blocks, ggml_type, rows, cols, dtype):
+    return blocks.new_empty((rows, cols), dtype=dtype)
+@torch.library.register_fake(add_op_namespace_prefix("mul_mat_vec"))
+def _mul_mat_vec_fake(blocks, x, ggml_type, out_features):
+    return x.new_empty((x.shape[0], out_features), dtype=torch.float32)
+@torch.library.register_fake(add_op_namespace_prefix("mul_mat_id"))
+def _mul_mat_id_fake(blocks, x, ids, ggml_type, out_features):
+    return x.new_empty((x.shape[0], ids.shape[1], out_features), dtype=torch.float32)

build/torch214-metal-aarch64-darwin/_ggml_quantization_metal_fe2bb66.abi3.so ADDED Viewed

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

+version https://git-lfs.github.com/spec/v1
+oid sha256:a49c6dd6100e61b0f9f7c5c0c72b3edcc72579d583045a81cdc8f78718bbbeb6
+size 3347704

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

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

+import torch
+from . import _ggml_quantization_metal_fe2bb66
+ops = torch.ops._ggml_quantization_metal_fe2bb66
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_ggml_quantization_metal_fe2bb66::{op_name}"

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

	@@ -0,0 +1,32 @@

+{
+  "name": "ggml-quantization",
+  "id": "_ggml_quantization_metal_fe2bb66",
+  "version": 1,
+  "kernels-minver": "0.14.0",
+  "license": "MIT",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "metal"
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
+      "__init__.py": "iPXgZXBgY3CzpNUSjmCovMVNPLBol1KBaNoTzkXp5mM=",
+      "_ggml_quantization_metal_fe2bb66.abi3.so": "pJxt1hAOYbD598XAxys+3McledWDBFqBzcj3hxi7vrY=",
+      "_ops.py": "7JPqKK8z3jmEj5qhydHkeSXOsZMXb424/1/5MR/BZuk=",
+      "symbols.json": "y6E2ftRiVpYGHN9aFeGUjoXci6YSNZRswRiLbHiaxFw="
+    }
+  },
+  "provenance": {
+    "kernel-builder": {
+      "version": "0.18.0-dev0",
+      "commit": "d054906a13710b811b96697b4635f03020842733",
+      "dirty": false
+    },
+    "kernel": {
+      "commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
+      "dirty": false
+    }
+  }
+}

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

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

	@@ -0,0 +1,169 @@

+{
+  "schema_version": 1,
+  "module": "_ggml_quantization_metal_fe2bb66",
+  "functions": [
+    {
+      "name": "dequantize",
+      "qualname": "dequantize",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "`(rows, bytes_per_row)` uint8 blocks -> `(rows, cols)` values of `dtype`.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "rows",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "cols",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "dtype",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.dtype"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    },
+    {
+      "name": "get_rows",
+      "qualname": "get_rows",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "The rows `indices` names, unpacked: `(rows, bytes_per_row)` uint8 -> `(len(indices), cols)`.\n\nggml's `get_rows`, which dequantizes as it gathers -- so reading a few rows out of a large table\nnever touches the rest.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "indices",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "cols",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "dtype",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.dtype"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    },
+    {
+      "name": "mul_mat_id",
+      "qualname": "mul_mat_id",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "One dispatch for a bank of routed experts: ggml's `mul_mv_id`.\n\n`blocks` is `(n_experts, out_features, bytes_per_row)`, `x` is `(n_tokens, in_features)`, and\n`ids` is `(n_tokens, n_used)` naming the expert each of a token's slots picked. The result is\n`(n_tokens, n_used, out_features)` f32, one row per slot.\n\nThe alternative is a gemv per expert per layer, whose arithmetic is dwarfed by the dispatch\naround it -- which is most of what a MoE decode step costs.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "x",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ids",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "out_features",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    },
+    {
+      "name": "mul_mat_vec",
+      "qualname": "mul_mat_vec",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "Fused dequantize-gemv: `x @ blocks.T` without unpacking `blocks`.\n\n`x` is `(rows, in_features)` and `rows` must be at most `MAX_GEMV_ROWS`. The result is f32\nwhatever `x`'s dtype was, since the kernel writes an f32 destination; cast it at the call site,\nwhere the cast can be fused into whatever consumes it.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "x",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "out_features",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    }
+  ],
+  "layers": []
+}

build/torch215-metal-aarch64-darwin/__init__.py ADDED Viewed

	@@ -0,0 +1,98 @@

+"""Compute directly on the packed blocks of a GGUF checkpoint.
+A GGUF weight is stored as blocks of 32 or 256 values sharing a scale. These kernels read that
+layout as it is, so a quantized model can be loaded and run without ever materializing a dense
+copy — which is the whole memory saving.
+Ported from llama.cpp's ggml-cuda; `vendor/UPSTREAM` pins the revision. The ops are named for what
+they do rather than for a backend: each backend registers its own implementation of the same
+schema, so calls dispatch on the tensor's device.
+"""
+import torch
+from ._ops import add_op_namespace_prefix, ops
+__all__ = ["GEMV_TYPES", "MAX_GEMV_ROWS", "dequantize", "get_rows", "mul_mat_id", "mul_mat_vec"]
+# Upstream's MMVQ_MAX_BATCH_SIZE: `mul_mat_vec` has no implementation beyond this many rows, so a
+# caller with more (prefill) dequantizes and uses an ordinary matmul.
+MAX_GEMV_ROWS = 8
+# ggml type ids `mul_mat_vec` implements, as this build reports them -- it is backend-specific, and
+# a type routed into a gemv that has no kernel for it is a fault, not a fallback. CUDA covers
+# Q4_0/Q4_1/Q5_0/Q5_1/Q8_0, the K quants, the IQ quants, MXFP4/NVFP4 and Q1_0/Q2_0; Metal is the
+# same minus NVFP4/Q1_0/Q2_0, which its metallib has no kernels for. `dequantize` covers more, so
+# check this before choosing the fused path.
+try:
+    GEMV_TYPES = frozenset(ops.gemv_types())
+except AttributeError:
+    # A build published before `gemv_types` existed; those are CUDA-only, so its list is theirs.
+    GEMV_TYPES = frozenset({2, 3, 6, 7, 8, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 29, 39, 40, 41, 42})
+def get_rows(
+    blocks: torch.Tensor, indices: torch.Tensor, ggml_type: int, cols: int, dtype: torch.dtype
+) -> torch.Tensor:
+    """The rows `indices` names, unpacked: `(rows, bytes_per_row)` uint8 -> `(len(indices), cols)`.
+    ggml's `get_rows`, which dequantizes as it gathers -- so reading a few rows out of a large table
+    never touches the rest.
+    """
+    return ops.get_rows(blocks, indices, ggml_type, cols, dtype)
+def dequantize(
+    blocks: torch.Tensor, ggml_type: int, rows: int, cols: int, dtype: torch.dtype
+) -> torch.Tensor:
+    """`(rows, bytes_per_row)` uint8 blocks -> `(rows, cols)` values of `dtype`."""
+    return ops.dequantize(blocks, ggml_type, rows, cols, dtype)
+def mul_mat_vec(
+    blocks: torch.Tensor, x: torch.Tensor, ggml_type: int, out_features: int
+) -> torch.Tensor:
+    """Fused dequantize-gemv: `x @ blocks.T` without unpacking `blocks`.
+    `x` is `(rows, in_features)` and `rows` must be at most `MAX_GEMV_ROWS`. The result is f32
+    whatever `x`'s dtype was, since the kernel writes an f32 destination; cast it at the call site,
+    where the cast can be fused into whatever consumes it.
+    """
+    return ops.mul_mat_vec(blocks, x, ggml_type, out_features)
+def mul_mat_id(
+    blocks: torch.Tensor, x: torch.Tensor, ids: torch.Tensor, ggml_type: int, out_features: int
+) -> torch.Tensor:
+    """One dispatch for a bank of routed experts: ggml's `mul_mv_id`.
+    `blocks` is `(n_experts, out_features, bytes_per_row)`, `x` is `(n_tokens, in_features)`, and
+    `ids` is `(n_tokens, n_used)` naming the expert each of a token's slots picked. The result is
+    `(n_tokens, n_used, out_features)` f32, one row per slot.
+    The alternative is a gemv per expert per layer, whose arithmetic is dwarfed by the dispatch
+    around it -- which is most of what a MoE decode step costs.
+    """
+    return ops.mul_mat_id(blocks, x, ids, ggml_type, out_features)
+# Without these, torch.compile cannot trace the ops and breaks the graph at every call.
+@torch.library.register_fake(add_op_namespace_prefix("get_rows"))
+def _get_rows_fake(blocks, indices, ggml_type, cols, dtype):
+    return blocks.new_empty((indices.numel(), cols), dtype=dtype)
+@torch.library.register_fake(add_op_namespace_prefix("dequantize"))
+def _dequantize_fake(blocks, ggml_type, rows, cols, dtype):
+    return blocks.new_empty((rows, cols), dtype=dtype)
+@torch.library.register_fake(add_op_namespace_prefix("mul_mat_vec"))
+def _mul_mat_vec_fake(blocks, x, ggml_type, out_features):
+    return x.new_empty((x.shape[0], out_features), dtype=torch.float32)
+@torch.library.register_fake(add_op_namespace_prefix("mul_mat_id"))
+def _mul_mat_id_fake(blocks, x, ids, ggml_type, out_features):
+    return x.new_empty((x.shape[0], ids.shape[1], out_features), dtype=torch.float32)

build/torch215-metal-aarch64-darwin/_ggml_quantization_metal_fe2bb66.abi3.so ADDED Viewed

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

+version https://git-lfs.github.com/spec/v1
+oid sha256:9ba194e4ec346cebbcff20a4d5309e7063e2ef45233dd2cfd08f376ab1347a3a
+size 3347704

build/torch215-metal-aarch64-darwin/_ops.py ADDED Viewed

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

+import torch
+from . import _ggml_quantization_metal_fe2bb66
+ops = torch.ops._ggml_quantization_metal_fe2bb66
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_ggml_quantization_metal_fe2bb66::{op_name}"

build/torch215-metal-aarch64-darwin/metadata.json ADDED Viewed

	@@ -0,0 +1,32 @@

+{
+  "name": "ggml-quantization",
+  "id": "_ggml_quantization_metal_fe2bb66",
+  "version": 1,
+  "kernels-minver": "0.14.0",
+  "license": "MIT",
+  "python-depends": [],
+  "kernel-depends": [],
+  "backend": {
+    "type": "metal"
+  },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
+      "__init__.py": "iPXgZXBgY3CzpNUSjmCovMVNPLBol1KBaNoTzkXp5mM=",
+      "_ggml_quantization_metal_fe2bb66.abi3.so": "m6GU5Ow0bOu8/yCk1TCecGPi70UjPdLP0I83arE0ejo=",
+      "_ops.py": "7JPqKK8z3jmEj5qhydHkeSXOsZMXb424/1/5MR/BZuk=",
+      "symbols.json": "y6E2ftRiVpYGHN9aFeGUjoXci6YSNZRswRiLbHiaxFw="
+    }
+  },
+  "provenance": {
+    "kernel-builder": {
+      "version": "0.18.0-dev0",
+      "commit": "d054906a13710b811b96697b4635f03020842733",
+      "dirty": false
+    },
+    "kernel": {
+      "commit": "fe2bb66fee6c2a77eccf08a0508dc57428d488ed",
+      "dirty": false
+    }
+  }
+}

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build/torch215-metal-aarch64-darwin/symbols.json ADDED Viewed

	@@ -0,0 +1,169 @@

+{
+  "schema_version": 1,
+  "module": "_ggml_quantization_metal_fe2bb66",
+  "functions": [
+    {
+      "name": "dequantize",
+      "qualname": "dequantize",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "`(rows, bytes_per_row)` uint8 blocks -> `(rows, cols)` values of `dtype`.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "rows",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "cols",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "dtype",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.dtype"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    },
+    {
+      "name": "get_rows",
+      "qualname": "get_rows",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "The rows `indices` names, unpacked: `(rows, bytes_per_row)` uint8 -> `(len(indices), cols)`.\n\nggml's `get_rows`, which dequantizes as it gathers -- so reading a few rows out of a large table\nnever touches the rest.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "indices",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "cols",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "dtype",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.dtype"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    },
+    {
+      "name": "mul_mat_id",
+      "qualname": "mul_mat_id",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "One dispatch for a bank of routed experts: ggml's `mul_mv_id`.\n\n`blocks` is `(n_experts, out_features, bytes_per_row)`, `x` is `(n_tokens, in_features)`, and\n`ids` is `(n_tokens, n_used)` naming the expert each of a token's slots picked. The result is\n`(n_tokens, n_used, out_features)` f32, one row per slot.\n\nThe alternative is a gemv per expert per layer, whose arithmetic is dwarfed by the dispatch\naround it -- which is most of what a MoE decode step costs.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "x",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ids",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "out_features",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    },
+    {
+      "name": "mul_mat_vec",
+      "qualname": "mul_mat_vec",
+      "module": "_ggml_quantization_metal_fe2bb66",
+      "kind": "function",
+      "doc": "Fused dequantize-gemv: `x @ blocks.T` without unpacking `blocks`.\n\n`x` is `(rows, in_features)` and `rows` must be at most `MAX_GEMV_ROWS`. The result is f32\nwhatever `x`'s dtype was, since the kernel writes an f32 destination; cast it at the call site,\nwhere the cast can be fused into whatever consumes it.",
+      "signature": {
+        "parameters": [
+          {
+            "name": "blocks",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "x",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "torch.Tensor"
+          },
+          {
+            "name": "ggml_type",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          },
+          {
+            "name": "out_features",
+            "kind": "POSITIONAL_OR_KEYWORD",
+            "default": null,
+            "annotation": "int"
+          }
+        ],
+        "return_annotation": "torch.Tensor"
+      }
+    }
+  ],
+  "layers": []
+}