[KernelGen][MThreads] Add permute_copy Moore Threads specialized operator

src/flag_gems/runtime/backend/_mthreads/ops/__init__.py

@@ -42,6 +42,7 @@
 from .one_hot import one_hot
 from .ones import ones
 from .ones_like import ones_like
+from .permute_copy import permute_copy
 from .prod import prod, prod_dim
 from .rand import rand
 from .rand_like import rand_like
@@ -105,6 +106,7 @@
     "one_hot",
     "ones",
     "ones_like",
+    "permute_copy",
     "prod",
     "prod_dim",
     "rand",

src/flag_gems/runtime/backend/_mthreads/ops/permute_copy.py

@@ -0,0 +1,296 @@
+# Copyright 2026 FlagOS Contributors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+from typing import Tuple
+
+import torch
+import triton
+import triton.language as tl
+
+from flag_gems.ops.permute_copy import permute_copy as default_permute_copy
+from flag_gems.runtime import torch_device_fn
+from flag_gems.utils import libentry
+
+logger = logging.getLogger(
+    f'flag_gems.runtime.backend._mthreads.ops.{__name__.split(".")[-1]}'
+)
+
+# Moore Threads hardware does not support fp64; int64 is supported for
+# indexing, but we keep the supported dtype set aligned with the other
+# mthreads specialized operators and fall back to the generic impl
+# otherwise.
+_SUPPORTED_DTYPES = {
+    torch.float16,
+    torch.bfloat16,
+    torch.float32,
+    torch.int32,
+    torch.int64,
+    torch.bool,
+}
+
+# Highest tensor rank handled directly by the specialized kernel. Tensors
+# with a larger rank fall back to the generic implementation.
+_MAX_KERNEL_RANK = 5
+
+
+@libentry()
+@triton.autotune(
+    configs=[
+        # BLOCK_M = 1: one output row per program, large inner block. Best when
+        # the last output dimension (N) is large, giving a long coalesced run.
+        triton.Config({"BLOCK_M": 1, "BLOCK_N": 512}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 1, "BLOCK_N": 1024}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 1, "BLOCK_N": 1024}, num_warps=8, num_stages=2),
+        triton.Config({"BLOCK_M": 1, "BLOCK_N": 2048}, num_warps=8, num_stages=2),
+        # BLOCK_M = 1, small inner block, 2 warps: best for rank-3 permutes
+        # where the last output dim is small (N ~= 64) — a short coalesced run
+        # with minimal thread overhead.
+        triton.Config({"BLOCK_M": 1, "BLOCK_N": 128}, num_warps=2, num_stages=2),
+        triton.Config({"BLOCK_M": 1, "BLOCK_N": 256}, num_warps=2, num_stages=2),
+        # BLOCK_M = 2 / 4: a few rows per program, mid-sized inner block.
+        triton.Config({"BLOCK_M": 2, "BLOCK_N": 512}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 2, "BLOCK_N": 1024}, num_warps=8, num_stages=2),
+        triton.Config({"BLOCK_M": 4, "BLOCK_N": 256}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 4, "BLOCK_N": 512}, num_warps=4, num_stages=2),
+        # BLOCK_M = 8 / 16: many rows per program, small inner block. Best when
+        # the last output dimension is small (e.g. rank-3 permutes with N=64),
+        # so each program still has enough work without wasting threads on
+        # masked-out elements.
+        triton.Config({"BLOCK_M": 8, "BLOCK_N": 128}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 8, "BLOCK_N": 256}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 16, "BLOCK_N": 128}, num_warps=4, num_stages=2),
+        triton.Config({"BLOCK_M": 16, "BLOCK_N": 256}, num_warps=8, num_stages=2),
+    ],
+    key=["n_elements", "N", "dtype_size"],
+)
+@triton.jit
+def _permute_copy_kernel(
+    src_ptr,
+    dst_ptr,
+    n_elements,
+    M,
+    N,
+    dtype_size,
+    # Outer (all-but-last) output shapes, padded to 4 with leading 1s.
+    out_shape0,
+    out_shape1,
+    out_shape2,
+    out_shape3,
+    # Reordered source strides (``src_stride_j = input.stride(perm[j])``),
+    # padded to 5 with leading 0s; index 4 is the inner (last) dim.
+    src_stride0,
+    src_stride1,
+    src_stride2,
+    src_stride3,
+    src_stride4,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+):
+    pid_m = tl.program_id(0)
+    pid_n = tl.program_id(1)
+
+    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)  # outer flat index
+    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)  # last-dim index
+    mask_m = rm < M
+    mask_n = rn < N
+    mask = mask_m[:, None] & mask_n[None, :]
+
+    # Decompose the outer flat index into per-dimension output indices. This is
+    # done once per program (scalar work over BLOCK_M rows) rather than per
+    # element, which is the key difference from a flat 1-D kernel and keeps the
+    # inner-dim load/store loop free of integer divisions. Leading size-1
+    # dimensions (padding for ranks < 5) simply yield index 0.
+    rem = rm
+    o3 = rem % out_shape3
+    rem = rem // out_shape3
+    o2 = rem % out_shape2
+    rem = rem // out_shape2
+    o1 = rem % out_shape1
+    rem = rem // out_shape1
+    o0 = rem % out_shape0
+
+    # Source offset: dot product of the (reordered) source strides with the
+    # output indices. The outer part is constant across the inner dim, so it
+    # forms a per-row base; the inner part is the strided last-dim access.
+    src_base = o0 * src_stride0 + o1 * src_stride1 + o2 * src_stride2 + o3 * src_stride3
+    src_offset = src_base[:, None] + rn[None, :] * src_stride4
+
+    # Destination offset: the output is row-major contiguous, so the flat index
+    # is simply ``outer_index * N + inner_index`` (inner stride == 1).
+    dst_offset = rm[:, None] * N + rn[None, :]
+
+    vals = tl.load(src_ptr + src_offset, mask=mask)
+    tl.store(dst_ptr + dst_offset, vals, mask=mask)
+
+
+@libentry()
+@triton.autotune(
+    configs=[
+        # For rank-3 permutes the output is small and launch-bound, so a tight
+        # 3-D grid (one program per (d0, d1) cell, BLOCK_N covering the last
+        # dim) with few warps minimizes per-program overhead and avoids the
+        # integer divisions of the flat-index decomposition above.
+        # num_warps=2 gives the best warm latency on Moore Threads for the
+        # small per-program workloads (one row of the last dim).
+        triton.Config({"BLOCK_N": 64}, num_warps=2, num_stages=2),
+        triton.Config({"BLOCK_N": 128}, num_warps=2, num_stages=2),
+        triton.Config({"BLOCK_N": 256}, num_warps=2, num_stages=2),
+    ],
+    key=["n_elements", "d2", "dtype_size"],
+)
+@triton.jit
+def _permute_copy_kernel_3d(
+    src_ptr,
+    dst_ptr,
+    n_elements,
+    d0,
+    d1,
+    d2,
+    dtype_size,
+    # Reordered source strides (``src_stride_j = input.stride(perm[j])``).
+    src_stride0,
+    src_stride1,
+    src_stride2,
+    # Output strides (row-major contiguous).
+    out_stride0,
+    out_stride1,
+    out_stride2,
+    BLOCK_N: tl.constexpr,
+):
+    p0 = tl.program_id(0)  # output dim 0
+    p1 = tl.program_id(1)  # output dim 1
+    p2 = tl.program_id(2)  # output dim 2 (blocked)
+
+    rn = p2 * BLOCK_N + tl.arange(0, BLOCK_N)
+    mask = rn < d2
+
+    # Source offset: dot product of the reordered source strides with the
+    # output indices. p0/p1 are scalar per program; rn is the inner run.
+    src_offset = p0 * src_stride0 + p1 * src_stride1 + rn * src_stride2
+    # Destination offset: the output is row-major contiguous.
+    dst_offset = p0 * out_stride0 + p1 * out_stride1 + rn * out_stride2
+
+    vals = tl.load(src_ptr + src_offset, mask=mask)
+    tl.store(dst_ptr + dst_offset, vals, mask=mask)
+
+
+def _use_triton_kernel(x: torch.Tensor, dims) -> Tuple[bool, int]:
+    if not isinstance(x, torch.Tensor):
+        return False, 0
+    if x.device.type != "musa" or x.dtype not in _SUPPORTED_DTYPES:
+        return False, 0
+    if x.ndim == 0 or x.ndim > _MAX_KERNEL_RANK:
+        return False, 0
+    if x.numel() == 0 or not x.is_contiguous():
+        return False, 0
+    # ``dims`` must be a genuine permutation of range(ndim); otherwise the
+    # copy semantics differ from ``aten::permute_copy``.
+    ndim = x.ndim
+    try:
+        normalized = [d if d >= 0 else d + ndim for d in dims]
+    except TypeError:
+        return False, 0
+    if sorted(normalized) != list(range(ndim)) or len(normalized) != ndim:
+        return False, 0
+    return True, x.element_size()
+
+
+def _launch_permute_copy(
+    x: torch.Tensor, out: torch.Tensor, dims, dtype_size: int
+) -> torch.Tensor:
+    ndim = x.ndim
+
+    # Reorder the input strides by the permutation so the kernel can compute
+    # the source offset as a plain dot product of the output indices.
+    in_strides = list(x.stride())
+    src_strides = [in_strides[dims[j]] for j in range(ndim)]
+    out_strides = list(out.stride())
+    out_shapes = list(out.shape)
+    n_elements = out.numel()
+
+    with torch_device_fn.device(out.device):
+        if ndim == 3:
+            # Rank-3 fast path: a 3-D grid with no flat-index decomposition.
+            grid = lambda meta: (
+                out_shapes[0],
+                out_shapes[1],
+                triton.cdiv(out_shapes[2], meta["BLOCK_N"]),
+            )
+            _permute_copy_kernel_3d[grid](
+                x,
+                out,
+                n_elements,
+                out_shapes[0],
+                out_shapes[1],
+                out_shapes[2],
+                dtype_size,
+                src_strides[0],
+                src_strides[1],
+                src_strides[2],
+                out_strides[0],
+                out_strides[1],
+                out_strides[2],
+            )
+            return out
+
+        # General path for ranks 1, 2, 4, 5: pad shapes/strides up to the fixed
+        # 5-D kernel signature. The inner (last) dimension is kept real; leading
+        # dimensions are padded with shape 1 (index always 0) and stride 0 (no
+        # offset contribution).
+        pad_n = _MAX_KERNEL_RANK - ndim
+        out_shapes_pad = [1] * pad_n + out_shapes
+        src_strides_pad = [0] * pad_n + src_strides
+
+        # Outer = all but the last dim; inner = last dim.
+        M = 1
+        for s in out_shapes_pad[:-1]:
+            M = M * s
+        N = out_shapes_pad[-1]
+
+        grid = lambda meta: (
+            triton.cdiv(M, meta["BLOCK_M"]),
+            triton.cdiv(N, meta["BLOCK_N"]),
+        )
+        _permute_copy_kernel[grid](
+            x,
+            out,
+            n_elements,
+            M,
+            N,
+            dtype_size,
+            out_shapes_pad[0],
+            out_shapes_pad[1],
+            out_shapes_pad[2],
+            out_shapes_pad[3],
+            src_strides_pad[0],
+            src_strides_pad[1],
+            src_strides_pad[2],
+            src_strides_pad[3],
+            src_strides_pad[4],
+        )
+    return out
+
+
+def permute_copy(x: torch.Tensor, dims):
+    logger.debug("GEMS_MTHREADS PERMUTE_COPY")
+    use_triton, dtype_size = _use_triton_kernel(x, dims)
+    if not use_triton:
+        return default_permute_copy(x, dims)
+
+    ndim = x.ndim
+    normalized = [d if d >= 0 else d + ndim for d in dims]
+    out_shape = [x.shape[d] for d in normalized]
+    out = torch.empty(out_shape, dtype=x.dtype, device=x.device)
+    return _launch_permute_copy(x, out, normalized, dtype_size)