mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-24 07:04:00 +08:00
Yongqi Zhuo
b1e1e250a2
* Add split-k for example_warp_specialize_flashmla * Rotate QK issue to end of main loop to allow more latency hiding with TMA issue * During rescale, read max score from REG instead of SMEM * Move K_pe_shared_1 load * Use stmatrix for SP0 write to reduce SMEM bank conflict * Support arbitrary SMEM layout in MMA lowering * Remove runtime guard for increase_descriptor_offset
837 lines
38 KiB
Python
837 lines
38 KiB
Python
import pytest
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import tilelang.testing
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import tvm
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from tvm import tirx
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from tilelang.layout import Layout
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from tilelang.layout import cute
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from tilelang.layout.swizzle import (
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make_full_bank_swizzled_layout,
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make_half_bank_swizzled_layout,
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make_quarter_bank_swizzled_layout,
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)
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from tilelang.intrinsics import make_mma_swizzle_layout
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tilelang.testing.set_random_seed()
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _swizzle(mode):
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"""The (b_bits, m_base, s_shift) triple of a ComposedLayout's swizzle."""
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sw = mode.swizzle
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return (sw.b_bits, sw.m_base, sw.s_shift)
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def _assert_struct(actual, shape, stride):
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"""Assert a flat layout structurally equals make_layout(shape, stride)."""
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assert tvm.ir.structural_equal(actual, cute.make_layout(shape, stride=stride)), f"{actual} != {cute.make_layout(shape, stride=stride)}"
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def _assert_same_fn(result, ref):
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"""Assert two layouts are the same function: equal domain size and equal
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image at every coordinate. Works for hierarchical results that cannot be
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constructed with the flat make_layout."""
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assert cute.size(result) == cute.size(ref), f"size {cute.size(result)} != {cute.size(ref)}"
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for i in range(cute.size(ref)):
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assert result(i) == ref(i), f"at {i}: {result(i)} != {ref(i)}"
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def _build_swizzled_layout(shape, intermediate_fn, b_bits, m_base, s_shift):
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"""A single-output Layout whose forward index swizzles a bijective pow2
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intermediate address, applying the swizzle exactly as SwizzleNode::Apply:
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apply(x) = x ^ ((x & yyy) >> s_shift), with yyy = mask << (m_base + s_shift)
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and mask = (1 << b_bits) - 1."""
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def forward(*vars):
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addr = intermediate_fn(*vars)
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mask = (1 << b_bits) - 1
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yyy = mask << (m_base + s_shift)
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return addr ^ ((addr & yyy) >> s_shift)
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return Layout(shape, forward)
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# ---------------------------------------------------------------------------
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# to_python / from_python: round-trip nested shapes; a layout exposes its
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# shape/stride as raw IntTuples.
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# ---------------------------------------------------------------------------
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def test_from_python_roundtrips_to_python():
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# from_python is the inverse of to_python for nested shapes.
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assert cute.to_python(cute.from_python([2, (3, 4)])) == (2, (3, 4))
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# A layout exposes its shape/stride as raw IntTuples for algebra.
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L = cute.make_layout((2, 3))
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assert L.shape == (2, 3)
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assert L.stride == (1, 2)
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# ---------------------------------------------------------------------------
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# IntTuple: construction, unpacking, and CuTe arithmetic (operator + / *).
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# ---------------------------------------------------------------------------
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def test_int_tuple_scalar_arithmetic():
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# Scalars add and multiply their values; 0 is the additive identity.
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assert cute.to_python(cute.from_python(3) + cute.from_python(4)) == 7
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assert cute.to_python(cute.from_python(3) * cute.from_python(4)) == 12
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assert cute.to_python(cute.from_python(5) + 0) == 5 # operand coercion + identity
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assert cute.to_python(2 * cute.from_python(6)) == 12 # __rmul__
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def test_int_tuple_tuple_arithmetic():
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# Tuple + tuple adds component-wise; the shorter operand is zero-padded.
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a = cute.from_python([2, 3])
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assert cute.to_python(a + cute.from_python([4, 5])) == (6, 8)
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assert cute.to_python(a + cute.from_python([10])) == (12, 3) # (2,3)+(10,) -> (12,3)
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def test_int_tuple_scaled_basis_arithmetic():
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# ScaledBasis terms expand to ArithmeticTuples and add (CuTe
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# as_arithmetic_tuple): same axis sums into one slot, distinct axes spread.
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same = cute.from_python(cute.E(0)) + cute.from_python(cute.ScaledBasis(2, 0))
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assert cute.to_python(same) == (3,) # 1@0 + 2@0 -> (1)+(2) -> (3)
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spread = cute.from_python(cute.E(0)) + cute.from_python(cute.E(1))
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assert cute.to_python(spread) == (1, 1) # 1@0 + 1@1 -> (1)+(0,1) -> (1,1)
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# ---------------------------------------------------------------------------
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# product / flatten_to_tuple: leaf product of a raw shape; collapse a
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# hierarchical shape/stride to a flat tuple of leaves.
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# ---------------------------------------------------------------------------
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def test_size_and_product():
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# size() is the domain of a Layout / ComposedLayout; product() is the leaf
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# product of a raw shape (flat, nested, or a scalar).
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assert cute.size(cute.make_layout((4, 8))) == 32
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assert cute.product((4, 8)) == 32
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assert cute.product((2, (2, 2))) == 8
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assert cute.product(5) == 5
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# Both thread PrimExprs for dynamic extents.
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n = tvm.tirx.Var("n", "int32")
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assert tvm.ir.structural_equal(cute.product((n, 4)), n * 4)
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assert tvm.ir.structural_equal(cute.size(cute.make_layout((n, 4), stride=(1, n))), n * 4)
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def test_flatten_to_tuple_collapses_hierarchy():
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# A hierarchical composition result flattens to a flat tuple of leaves.
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R = cute.composition(cute.make_layout((8, 8), stride=(8, 1)), cute.make_layout((2, 4), stride=(1, 4)))
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assert cute.flatten_to_tuple(R.shape) == tuple(int(s) for s in cute.flatten(R).shape)
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assert len(cute.flatten_to_tuple(R.shape)) == len(cute.flatten_to_tuple(R.stride))
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# ---------------------------------------------------------------------------
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# Layout: rank / size / layout[idx] / flatten, layout(coord) evaluation,
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# with_shape, and from_tilelang (recover a plain affine layout, else None).
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# ---------------------------------------------------------------------------
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def test_rank_size_getitem_flatten():
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L = cute.make_layout((4, 8), stride=(8, 1))
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assert cute.rank(L) == 2 and cute.size(L) == 32
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# layout[idx] is the i-th sublayout (a single mode unpacks to a scalar).
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assert L[0].shape == 4 and L[0].stride == 8
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assert L[1].shape == 8 and L[1].stride == 1
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# flatten of an already-flat layout is itself.
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assert tvm.ir.structural_equal(cute.flatten(L), L)
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def test_eval_plain_is_scalar():
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# layout(coord): idx2crd (mode 0 fastest) dotted with the strides.
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L = cute.make_layout((4, 8), stride=(8, 1))
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assert [L(c) for c in range(4)] == [0, 8, 16, 24]
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assert L(4) == 1 and L(7) == 25 and isinstance(L(4), int)
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def test_eval_tuple_coord():
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# crd2idx accepts a hierarchical coordinate (CuTe crd2idx_ttt), congruent to
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# the shape: layout((c0, c1)) == c0*stride0 + c1*stride1.
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L = cute.make_layout((4, 8), stride=(8, 1))
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assert L((1, 1)) == 9 and L((3, 7)) == 31
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assert all(L((c % 4, c // 4)) == L(c) for c in range(32))
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# Nested shape/stride with a nested coordinate.
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H = cute.make_layout((2, (2, 2)), stride=(1, (2, 4)))
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assert H((1, (1, 1))) == 1 + 2 + 4
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assert all(H(c) == H((c % 2, (c // 2 % 2, c // 4))) for c in range(8))
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def test_eval_dynamic_shape_and_stride():
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# Neither shape nor stride need be constant: crd2idx threads PrimExprs.
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s = tvm.tirx.Var("s", "int32")
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L = cute.make_layout((4,), stride=(s,))
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assert tvm.ir.structural_equal(L(2), 2 * s)
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assert tvm.ir.structural_equal(L((3,)), 3 * s)
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# Dynamic extent: the mode is sized by a Var.
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n = tvm.tirx.Var("n", "int32")
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D = cute.make_layout((n, 4), stride=(1, n))
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assert tvm.ir.structural_equal(D((2, 1)), 2 + n)
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def test_eval_basis_is_coordinate_tuple():
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# ScaledBasis strides make layout(coord) a coordinate: crd_k * (v@path)
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# lands v*crd_k in slot `path`, same-path contributions summing.
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ident = cute.make_identity_layout((4, 4))
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assert [ident(c) for c in range(16)] == [(c % 4, c // 4) for c in range(16)]
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# Permuted axes route mode k into the slot its basis names.
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perm = cute.make_layout((2, 3), stride=(cute.E(1), cute.E(0)))
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assert perm(5) == (2, 1) # crd=(1,2): 1@slot1, 2@slot0
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# Same path on both modes sums into one slot; a single touched axis stays
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# a (1-)tuple, never a bare scalar.
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same = cute.make_layout((2, 2), stride=(cute.E(0), cute.ScaledBasis(2, 0)))
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assert all(same(c) == (c,) for c in range(4))
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def test_with_shape_splits_contiguous_run():
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# A flat layout reinterpreted over a 2D shape: (8):(1) with_shape (2,4) ->
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# the column-major linearization keeps function identity.
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L = cute.make_layout(8, stride=1)
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r = L.with_shape((2, 4))
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assert r.shape == (2, 4)
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for c0 in range(2):
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for c1 in range(4):
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assert r((c0, c1)) == L(c0 + 2 * c1)
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def test_layout_from_tilelang_affine():
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L = cute.Layout.from_tilelang(Layout((16, 128), lambda i, j: i * 128 + j))
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assert L is not None
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_assert_struct(L, (16, 128), (128, 1))
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def test_layout_from_tilelang_rejects_swizzle():
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swz = _build_swizzled_layout((64, 512), lambda i, j: i * 512 + j, 3, 3, 3)
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assert cute.Layout.from_tilelang(swz) is None
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# ---------------------------------------------------------------------------
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# coalesce: ported from CuTe pycute test_coalesce (flat cases). coalesce never
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# changes layout(i), and a full collapse yields the scalar (1):(0).
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# ---------------------------------------------------------------------------
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@pytest.mark.parametrize(
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"shape,stride",
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[
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((1,), (0,)),
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((1,), (1,)),
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((2, 4), (1, 2)),
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((2, 4, 6), (1, 2, 8)),
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((2, 4, 6), (1, 6, 2)),
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((2, 1, 6), (1, 7, 2)),
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((2, 1, 6), (4, 7, 8)),
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((2, 4), (4, 1)),
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((2, 4, 6), (24, 6, 1)),
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((2, 1, 3), (2, 4, 4)),
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# Hierarchical operands (ported from CuTe pycute test_coalesce).
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((2, (4, 6)), None), # default (compact column-major) nested stride
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(((2, 2), (2, 2)), ((1, 4), (8, 32))),
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],
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)
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def test_coalesce_preserves_function(shape, stride):
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L = cute.make_layout(shape, stride=stride)
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_assert_same_fn(cute.coalesce(L), L)
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def test_coalesce_structural_results():
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# Contiguous bit-modes fuse to one; non-contiguous stay split; a fully
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# size-1 layout collapses to the scalar 1:0. A single surviving mode is
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# scalar-shaped (CuTe bw_coalesce returns Layout<NewShape,NewStride>).
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_assert_struct(cute.coalesce(cute.make_layout((2, 2, 2, 2, 2), stride=(1, 2, 4, 8, 16))), 32, 1)
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_assert_struct(cute.coalesce(cute.make_layout((8, 8), stride=(64, 1))), (8, 8), (64, 1))
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_assert_struct(cute.coalesce(cute.make_layout((1, 1), stride=(5, 7))), 1, 0)
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# A nested layout flattens, then fuses the contiguous (2,2):(1,4) prefix into
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# one mode while the gapped last mode stays split (CuTe pycute coalesce).
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_assert_struct(cute.coalesce(cute.make_layout(((2, 2), (2, 2)), stride=((1, 4), (8, 32)))), (2, 4, 2), (1, 4, 32))
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# ---------------------------------------------------------------------------
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# right_inverse: ported from CuTe pycute test_right_inverse (flat cases).
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# Defining property: layout(inv(i)) == i for all i < size(inv).
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# ---------------------------------------------------------------------------
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@pytest.mark.parametrize(
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"shape,stride",
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[
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((1,), (0,)),
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((1, 1), (0, 0)),
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((3, 7), (0, 0)),
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((1,), (1,)),
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((4,), (0,)),
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((4,), (1,)),
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((4,), (2,)),
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((2, 4), (0, 2)),
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((8, 4), (1, 8)),
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((8, 4), (4, 1)),
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((2, 4, 6), (1, 2, 8)),
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((2, 4, 6), (4, 1, 8)),
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((4, 2), (1, 16)),
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((64, 8), (1, 64)),
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],
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)
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def test_right_inverse(shape, stride):
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L = cute.make_layout(shape, stride=stride)
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inv = cute.right_inverse(L)
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for i in range(cute.size(inv)):
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assert L(inv(i)) == i
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# ---------------------------------------------------------------------------
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# composition: ported from CuTe pycute test_composition (flat cases).
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# Defining property: (A o B)(i) == A(B(i)) for all i; size(A o B) == size(B).
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# ---------------------------------------------------------------------------
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@pytest.mark.parametrize(
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"ashape,astride,bshape,bstride",
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[
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((1,), (0,), (1,), (0,)),
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((1,), (1,), (1,), (1,)),
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((4,), (1,), (4,), (1,)),
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((4,), (2,), (4,), (1,)),
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((4,), (0,), (4,), (1,)),
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((4,), (1,), (2,), (2,)),
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((4,), (2,), (2,), (2,)),
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((12,), (1,), (4, 3), (3, 1)),
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((12,), (1,), (2, 3), (2, 4)),
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((12,), (2,), (4, 3), (1, 4)),
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((4, 3), (1, 4), (12,), (1,)),
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((4, 3), (1, 4), (6,), (2,)),
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((4, 3), (3, 1), (12,), (1,)),
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((4, 3), (3, 1), (6, 2), (2, 1)),
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((4, 8), (8, 1), (8, 4), (1, 8)),
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((2, 3, 4), (1, 2, 6), (6, 4), (1, 6)),
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((64, 8), (8, 1), (8, 64), (1, 8)),
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((8, 8), (8, 1), (2, 4), (1, 4)),
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((4, 8, 2), (1, 4, 32), (8,), (2,)),
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# LHS contiguous only after coalescing (coalesce_x with a scalar
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# coprofile before the peel).
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((2, 2), (1, 2), (4,), (1,)),
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((4, 4), (1, 4), (8,), (1,)),
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((3, 4), (1, 3), (6,), (1,)),
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# RHS reaches past the LHS domain via a trailing size-1 LHS mode (only
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# coalesce_x's shape-2 sentinel keeps the trailing slot).
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((4, 1), (1, 0), (4,), (1,)),
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((8, 1), (1, 0), (8,), (1,)),
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# Hierarchical operands (ported from CuTe pycute test_composition).
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((8, 8), (8, 1), ((2, 2, 2), (2, 2, 2)), ((1, 16, 4), (8, 2, 32))),
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(((4, 2),), ((1, 16),), (4, 2), (2, 1)),
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((4, 8, 2), (2, 8, 1), (2, 2, 2), (1, 8, 2)),
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((4, 6, 8), (1, 4, 7), (6,), (1,)),
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((4, 6, 8, 10), (2, 3, 5, 7), (6,), (12,)),
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# Nested LHS composed with a flat RHS (the lhs tree is addressed by the
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# rhs's reach, not its own rank).
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(((2, 2, 2), (2, 2, 2)), ((1, 16, 4), (8, 2, 32)), (8,), (4,)),
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# Default (compact column-major) LHS stride composed with a nested RHS.
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((8, 8), None, ((2, 2, 2), (2, 2, 2)), ((1, 16, 4), (8, 2, 32))),
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# Both operands rank-3, RHS reorders/strides into the LHS modes.
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((4, 8, 2), (2, 8, 1), (4, 2, 2), (2, 8, 1)),
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],
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)
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def test_composition_matches_bruteforce(ashape, astride, bshape, bstride):
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A = cute.make_layout(ashape, stride=astride)
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B = cute.make_layout(bshape, stride=bstride)
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R = cute.composition(A, B)
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# (A o B)(i) == A(B(i)) for all i; size(A o B) == size(B).
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assert cute.size(R) == cute.size(B)
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for i in range(cute.size(B)):
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assert R(i) == A(B(i))
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def test_composition_structural_results():
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# Structural checks for results whose RHS reaches past the LHS domain (CuTe
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# extends the last mode linearly, so the brute-force oracle -- which wraps --
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# cannot validate these): (4):(1) o (4):(2) == (4):(2).
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_assert_struct(cute.composition(cute.make_layout((4,), stride=(1,)), cute.make_layout((4,), stride=(2,))), (4,), (2,))
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# A row-major LHS sub-block: (4,3):(3,1) o (12):(1) flattens to (4,3):(3,1).
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R = cute.composition(cute.make_layout((4, 3), stride=(3, 1)), cute.make_layout((12,), stride=(1,)))
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assert cute.flatten_to_tuple(R.shape) == (4, 3)
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assert cute.flatten_to_tuple(R.stride) == (3, 1)
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def test_composition_scaled_basis_routing():
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# A ScaledBasis RHS routes each result mode into the LHS axis its path
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# names (basis_get), independent of mode order. E(1),E(0) swaps axes, so the
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# result strides follow the routing -> structurally [4,8]:[100,10].
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A = cute.make_layout((8, 4), stride=(10, 100))
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B = cute.make_layout((4, 8), stride=(cute.E(1), cute.E(0)))
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_assert_struct(cute.composition(A, B), (4, 8), (100, 10))
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# The identity RHS over [4,4] selects the [4,4] sub-block of A.
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A2 = cute.make_layout((16, 16), stride=(1, 16))
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R = cute.composition(A2, cute.make_identity_layout((4, 4)))
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_assert_struct(R, (4, 4), (1, 16))
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def test_composition_scaled_basis_dynamic_strides():
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# Identity RHS routes each mode into the matching LHS axis, so the result
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# strides are the LHS's dynamic strides s_m, s_n (the is_scaled_basis branch).
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s_m, s_n = tvm.tirx.Var("s_m", "int32"), tvm.tirx.Var("s_n", "int32")
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R = cute.composition(cute.make_layout((16, 16), stride=(s_m, s_n)), cute.make_identity_layout((4, 4)))
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assert R.shape == (4, 4)
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sm, sn = R.stride
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assert tvm.ir.structural_equal(sm, s_m) and tvm.ir.structural_equal(sn, s_n)
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def test_right_inverse_composition_smem_to_gmem():
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# The TMA building block: composing a GMEM layout with the inverse of a SMEM
|
|
# layout yields a SMEM-address -> GMEM-address map.
|
|
smem = cute.make_layout((8, 64), stride=(64, 1))
|
|
gmem = cute.make_layout((8, 64), stride=(1024, 1))
|
|
composite = cute.composition(gmem, cute.right_inverse(smem))
|
|
for c in range(cute.product((8, 64))):
|
|
assert composite(smem(c)) == gmem(c)
|
|
|
|
|
|
def test_tma_box_validity_via_composite():
|
|
# Coalesce(GMEM o RightInverse(SMEM)) decides TMA-expressibility: its
|
|
# innermost stride must be 1. Row-major SMEM passes; transposed fails.
|
|
gmem = cute.make_layout((8, 64), stride=(256, 1))
|
|
ok = cute.coalesce(cute.composition(gmem, cute.right_inverse(cute.make_layout((8, 64), stride=(64, 1)))))
|
|
bad = cute.coalesce(cute.composition(gmem, cute.right_inverse(cute.make_layout((8, 64), stride=(1, 8)))))
|
|
assert list(ok.stride)[0] == 1
|
|
assert list(bad.stride)[0] != 1
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Layout algebra ported from CuTe for the GMMA descriptor analysis:
|
|
# filter / cosize / complement / logical_divide. Reference values computed by
|
|
# hand from CuTe (layout.hpp): complement sort-and-fold, logical_divide =
|
|
# composition(layout, make_layout(tiler, complement(tiler, size(coalesce)))).
|
|
# ---------------------------------------------------------------------------
|
|
def test_filter_drops_trivial_modes():
|
|
# filter = coalesce(filter_zeros): drop stride-0 and size-1 modes.
|
|
_assert_struct(cute.filter(cute.make_layout((4, 1, 2), stride=(1, 0, 8))), (4, 2), (1, 8))
|
|
_assert_struct(cute.filter(cute.make_layout((1, 1), stride=(5, 7))), 1, 0)
|
|
|
|
|
|
def test_filter_keeps_dynamic_modes():
|
|
s = tvm.tirx.Var("s", "int32")
|
|
# A const-0 stride mode is dropped; the dynamic-stride mode survives.
|
|
_assert_struct(cute.filter(cute.make_layout((4, 1, 8), stride=(s, 0, 1))), (4, 8), (s, 1))
|
|
|
|
|
|
def test_cosize_matches_cute():
|
|
# cosize = 1 + sum_i (shape_i - 1) * |stride_i|.
|
|
assert cute.cosize(cute.make_layout((4, 2), stride=(1, 8))) == 12
|
|
assert cute.cosize(cute.make_layout((8, 8), stride=(8, 1))) == 64
|
|
assert cute.cosize(cute.make_layout((64, 8), stride=(8, 1))) == 512
|
|
|
|
|
|
def test_cosize_dynamic_is_symbolic():
|
|
s = tvm.tirx.Var("s", "int32")
|
|
# cosize = 1 + (4-1)*|s| + (8-1)*1 = 3*|s| + 8 ; stays a PrimExpr, and the
|
|
# |stride| term emits CuTe's abs(stride). Check by substituting s.
|
|
got = cute.cosize(cute.make_layout((4, 8), stride=(s, 1)))
|
|
assert isinstance(got, tirx.PrimExpr)
|
|
ana = tvm.arith.Analyzer()
|
|
for sv, expect in [(5, 3 * 5 + 8), (-5, 3 * 5 + 8)]:
|
|
sub = tvm.tirx.stmt_functor.substitute(got, {s: tvm.tirx.const(sv, "int32")})
|
|
assert int(ana.simplify(sub)) == expect
|
|
|
|
|
|
def test_complement_matches_cute():
|
|
# complement(layout, cotarget): the gaps the layout does not address.
|
|
_assert_same_fn(cute.complement(cute.make_layout(2, stride=1), 16), cute.make_layout(8, stride=2))
|
|
# 4:1 within 24 -> rest is 6:4.
|
|
_assert_same_fn(cute.complement(cute.make_layout(4, stride=1), 24), cute.make_layout(6, stride=4))
|
|
# Structural: a single surviving mode is SCALAR-shaped (CuTe bw_coalesce
|
|
# returns Layout<NewShape,NewStride>, not a 1-tuple).
|
|
_assert_struct(cute.complement(cute.make_layout(2, stride=1), 16), 8, 2)
|
|
_assert_struct(cute.complement(cute.make_layout(4, stride=1), 24), 6, 4)
|
|
|
|
|
|
def test_complement_rank1_dynamic():
|
|
s = tvm.tirx.Var("s", "int32")
|
|
# complement((4):(s), 1024) = coalesce((s, ceil_div(1024, 4s)):(1, 4s)).
|
|
comp = cute.complement(cute.make_layout(4, stride=s), 1024)
|
|
assert comp.shape[0] == s and comp.stride[0] == 1
|
|
# rest stride = 4*s ; rest extent = ceil_div(1024, 4*s) (symbolic).
|
|
ana = tvm.arith.Analyzer()
|
|
assert ana.simplify(comp.stride[1] - 4 * s) == 0
|
|
|
|
|
|
def test_complement_rank_gt1_dynamic_rejected():
|
|
s = tvm.tirx.Var("s", "int32")
|
|
# CuTe static_assert: dynamic-stride complement only for rank-1 layouts.
|
|
with pytest.raises(Exception, match="rank-1"):
|
|
cute.complement(cute.make_layout((4, 8), stride=(s, 1)), 1024)
|
|
|
|
|
|
def test_logical_divide_matches_cute():
|
|
# logical_divide splits a mode into (tile, rest).
|
|
_assert_same_fn(
|
|
cute.logical_divide(cute.make_layout(8, stride=1), cute.make_layout(2, stride=1)), cute.make_layout((2, 4), stride=(1, 2))
|
|
)
|
|
_assert_same_fn(
|
|
cute.logical_divide(cute.make_layout(8, stride=1), cute.make_layout(8, stride=1)), cute.make_layout((8, 1), stride=(1, 0))
|
|
)
|
|
# Structural: the result is FLAT (tile, rest), congruent to (_1, _1) -- the
|
|
# complement's single mode is scalar, so no spurious nesting (mirrors CuTe).
|
|
_assert_struct(cute.logical_divide(cute.make_layout(8, stride=1), cute.make_layout(2, stride=1)), (2, 4), (1, 2))
|
|
_assert_struct(cute.logical_divide(cute.make_layout(8, stride=1), cute.make_layout(8, stride=1)), (8, 1), (1, 0))
|
|
# Strided base (GMMA MN mode 128:4 divided by the W=8 tile) stays flat.
|
|
_assert_struct(cute.logical_divide(cute.make_layout(128, stride=4), cute.make_layout(8, stride=1)), (8, 16), (4, 32))
|
|
assert cute.congruent(cute.logical_divide(cute.make_layout(128, stride=4), cute.make_layout(8, stride=1)).shape, (1, 1))
|
|
|
|
|
|
def test_logical_divide_dynamic_layout_stride():
|
|
s = tvm.tirx.Var("s", "int32")
|
|
# Static shape (so size is concrete) but a dynamic stride: division still
|
|
# works because complement is on the (static) tiler and composition handles
|
|
# the dynamic layout stride. (8):(s) by (2):(1) -> (2, 4):(s, 2s).
|
|
res = cute.logical_divide(cute.make_layout(8, stride=s), cute.make_layout(2, stride=1))
|
|
ana = tvm.arith.Analyzer()
|
|
# Result is hierarchical: shape (2, (4,)), stride (s, (2s,)).
|
|
assert ana.simplify(res(0) - 0) == 0
|
|
assert ana.simplify(res(1) - s) == 0 # next tile element: stride s
|
|
assert ana.simplify(res(2) - 2 * s) == 0 # next rest element: stride 2s
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# make_layout / make_column_major / make_row_major / make_identity_layout, and
|
|
# the make_layout([layout, ...]) concat form.
|
|
# ---------------------------------------------------------------------------
|
|
def test_make_layout_default_stride_is_column_major():
|
|
# Omitting stride yields the compact column-major stride (mode 0 fastest).
|
|
assert tvm.ir.structural_equal(cute.make_layout((4, 8)), cute.make_column_major_layout((4, 8)))
|
|
_assert_struct(cute.make_layout((4, 8)), (4, 8), (1, 4))
|
|
|
|
|
|
def test_make_identity_layout_strides_are_unit_basis():
|
|
# Each identity stride is the unit basis E<k>.
|
|
L = cute.make_layout((4, 4), stride=(cute.E(0), cute.E(1)))
|
|
assert tvm.ir.structural_equal(L, cute.make_identity_layout((4, 4)))
|
|
for k, s in enumerate(L.stride):
|
|
assert isinstance(s, cute.ScaledBasis) and s.value == 1 and s.mode == (k,)
|
|
|
|
|
|
def test_make_layout_nested_column_row_identity():
|
|
# Column/row-major and identity over a nested shape produce congruent nested
|
|
# strides (CuTe compact_col_major / compact_row_major / make_identity_layout).
|
|
assert tvm.ir.structural_equal(cute.make_column_major_layout((2, (2, 2))), cute.make_layout((2, (2, 2)), stride=(1, (2, 4))))
|
|
assert tvm.ir.structural_equal(cute.make_row_major_layout((2, (2, 2))), cute.make_layout((2, (2, 2)), stride=(4, (2, 1))))
|
|
# Identity maps each linear coord to its full nested idx2crd coordinate.
|
|
I = cute.make_identity_layout((2, (2, 2)))
|
|
assert I.shape == (2, (2, 2))
|
|
assert [I(c) for c in range(8)] == [(c % 2, (c // 2 % 2, c // 4)) for c in range(8)]
|
|
# Dynamic extents thread through the makers.
|
|
n = tvm.tirx.Var("n", "int32")
|
|
assert tvm.ir.structural_equal(cute.make_column_major_layout((n, 4)), cute.make_layout((n, 4), stride=(1, n)))
|
|
|
|
|
|
def test_scaled_basis_and_int_expr_strides():
|
|
# A non-unit ScaledBasis stride round-trips its scale and mode.
|
|
(sb,) = cute.make_layout((2,), stride=(cute.ScaledBasis(64, 1),)).stride
|
|
assert isinstance(sb, cute.ScaledBasis) and sb.value == 64 and sb.mode == (1,)
|
|
# A dynamic (PrimExpr) stride round-trips structurally.
|
|
s = tvm.tirx.Var("s", "int32")
|
|
(leaf,) = cute.make_layout((8,), stride=(s,)).stride
|
|
assert tvm.ir.structural_equal(leaf, s)
|
|
|
|
|
|
def test_make_layout_concat():
|
|
a = cute.make_layout(64, stride=1)
|
|
b = cute.make_layout(4, stride=64)
|
|
cat = cute.make_layout([a, b])
|
|
assert cat.shape == (64, 4)
|
|
assert cat.stride == (1, 64)
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# ComposedLayout.recast: shift m_base by log2(old) - log2(new); b_bits /
|
|
# s_shift preserved (a linear layout recast is a no-op).
|
|
# ---------------------------------------------------------------------------
|
|
def test_recast_method():
|
|
# Recast shifts m_base by log2(old) - log2(new); b_bits / s_shift preserved.
|
|
mode = cute.ComposedLayout.from_tilelang(_build_swizzled_layout((64, 512), lambda i, j: (j % 64) + i * 64 + (j // 64) * 4096, 3, 3, 3))
|
|
assert _swizzle(mode) == (3, 3, 3)
|
|
assert _swizzle(mode.recast(16, 8)) == (3, 4, 3) # smaller elem -> m_base += 1
|
|
assert _swizzle(mode.recast(32, 8)) == (3, 5, 3) # m_base += 2
|
|
assert _swizzle(mode.recast(16, 16)) == (3, 3, 3) # same width -> no-op
|
|
|
|
|
|
def test_recast_linear_is_noop():
|
|
mode = cute.ComposedLayout.from_tilelang(Layout((16, 128), lambda i, j: i * 128 + j))
|
|
assert not mode.swizzle.is_swizzled
|
|
assert _swizzle(mode.recast(32, 8)) == (0, 0, 0)
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# ComposedLayout.from_tilelang: recover a Swizzle over an affine layout.
|
|
# Bank swizzles are Sw<b, 4, 3> in byte space (b = 1/2/3 for 32/64/128B).
|
|
# ---------------------------------------------------------------------------
|
|
@pytest.mark.parametrize(
|
|
"maker,b_bits",
|
|
[
|
|
(make_quarter_bank_swizzled_layout, 1),
|
|
(make_half_bank_swizzled_layout, 2),
|
|
(make_full_bank_swizzled_layout, 3),
|
|
],
|
|
)
|
|
def test_real_bank_swizzles(maker, b_bits):
|
|
# continuous = vector_size * 2^b columns (float16 => vector_size 8).
|
|
buf = tirx.decl_buffer((8, 8 * (1 << b_bits)), "float16", name="A", scope="shared")
|
|
mode = cute.ComposedLayout.from_tilelang(maker(buf), buf)
|
|
assert mode is not None and _swizzle(mode) == (b_bits, 4, 3)
|
|
|
|
|
|
@pytest.mark.parametrize("dtype", ["int8", "float16", "float32"])
|
|
def test_bank_swizzle_mbase_is_byte_invariant(dtype):
|
|
# m_base is 4 in byte space for every dtype (the swizzle acts at a fixed
|
|
# 128-bit vector granularity): int8 (4+0), float16 (3+1), float32 (2+2).
|
|
vector_size = 128 // int(tvm.DataType(dtype).bits)
|
|
buf = tirx.decl_buffer((8, vector_size * 8), dtype, name="A", scope="shared")
|
|
mode = cute.ComposedLayout.from_tilelang(make_full_bank_swizzled_layout(buf), buf)
|
|
assert mode is not None and _swizzle(mode) == (3, 4, 3)
|
|
|
|
|
|
@pytest.mark.parametrize("lead", [2, 3, 5])
|
|
def test_expanded_layout_with_nonpow2_leading_dim(lead):
|
|
# ExpandLayout2D prepends a (possibly non-pow2) batch dim; the trailing
|
|
# swizzled tile is still detected.
|
|
layout = make_full_bank_swizzled_layout(tirx.decl_buffer((8, 64), "float16", name="A", scope="shared")).expand([lead])
|
|
buf = tirx.decl_buffer((lead, 8, 64), "float16", name="A", scope="shared")
|
|
mode = cute.ComposedLayout.from_tilelang(layout, buf)
|
|
assert mode is not None and _swizzle(mode) == (3, 4, 3)
|
|
|
|
|
|
# make_mma_swizzle_layout is the canonical bank swizzle Sw<b, 4, 3>: the shift
|
|
# stays 3 (8-row period) and b follows the 16B-vector count per row -- full(8)/
|
|
# half(4)/quarter(2) -> b=3/2/1. Wider rows keep b=3 (the block index tiles into
|
|
# an outer mode), so every case stays hardware-expressible.
|
|
@pytest.mark.parametrize(
|
|
"dtype,cols,b_bits",
|
|
[
|
|
("float16", 32, 2),
|
|
("float16", 64, 3),
|
|
("float16", 128, 3),
|
|
("float16", 256, 3),
|
|
("float32", 32, 3),
|
|
("float32", 64, 3),
|
|
("int8", 64, 2),
|
|
("int8", 128, 3),
|
|
],
|
|
)
|
|
def test_mma_swizzle_layout(dtype, cols, b_bits):
|
|
buf = tirx.decl_buffer((16, cols), dtype, name="A", scope="shared")
|
|
mode = cute.ComposedLayout.from_tilelang(make_mma_swizzle_layout(buf), buf)
|
|
assert mode is not None and _swizzle(mode) == (b_bits, 4, 3)
|
|
|
|
|
|
@pytest.mark.parametrize("cols", [192, 384, 576])
|
|
def test_mma_swizzle_nonpow2_columns(cols):
|
|
# A non-pow2 column count (e.g. head_dim 192 = 64*3) tiles the swizzle into
|
|
# an outer mode of non-pow2 extent. The decoder must still find the swizzle,
|
|
# which lives in the addressable low pow2 prefix of the column dim -- probing
|
|
# only pow2-extent dims would miss it (regression: gqa_bwd TMA copy, PR #2380).
|
|
buf = tirx.decl_buffer((128, cols), "float16", name="A", scope="shared")
|
|
mode = cute.ComposedLayout.from_tilelang(make_mma_swizzle_layout(buf), buf)
|
|
assert mode is not None and _swizzle(mode) == (3, 4, 3)
|
|
|
|
|
|
def test_mma_swizzle_smooth_is_linear():
|
|
# is_smooth=True yields an identity layout, which is linear (not swizzled).
|
|
buf = tirx.decl_buffer((16, 64), "float16", name="A", scope="shared")
|
|
mode = cute.ComposedLayout.from_tilelang(make_mma_swizzle_layout(buf, is_smooth=True), buf)
|
|
assert mode is not None and not mode.swizzle.is_swizzled
|
|
|
|
|
|
def test_core_is_dtype_agnostic_and_buffer_recasts():
|
|
# The no-buffer core reports m_base in element-offset bits (3 for float16's
|
|
# vector_size 8); the buffer overload recasts it to byte-space 4.
|
|
buf = tirx.decl_buffer((8, 64), "float16", name="A", scope="shared")
|
|
layout = make_full_bank_swizzled_layout(buf)
|
|
assert _swizzle(cute.ComposedLayout.from_tilelang(layout)) == (3, 3, 3)
|
|
assert _swizzle(cute.ComposedLayout.from_tilelang(layout, buf)) == (3, 4, 3)
|
|
|
|
|
|
@pytest.mark.parametrize("OFFSET", [4096, 5 * 4096, 8192 + 4096])
|
|
def test_swizzle_with_nonzero_base_offset(OFFSET):
|
|
# A swizzle over a high, disjoint base offset: the base is carried as the
|
|
# ComposedLayout offset (offset = Sw(A(0))), not baked into the strides.
|
|
mode = cute.ComposedLayout.from_tilelang(_build_swizzled_layout((8, 64), lambda i, j: OFFSET + i * 64 + j, 3, 3, 3))
|
|
assert mode is not None and _swizzle(mode) == (3, 3, 3)
|
|
assert mode.offset == OFFSET
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"b_bits,m_base,s_shift",
|
|
[(b, m, s) for b in (1, 2, 3) for m in (0, 2, 4) for s in (1, 3, 4) if s >= b],
|
|
)
|
|
def test_synthetic_swizzle_sweep(b_bits, m_base, s_shift):
|
|
# Recovery is exact across many (b, m, s) on a clean row-major intermediate.
|
|
# Only s >= b (non-overlapping source/target), as in real swizzles.
|
|
W = m_base + s_shift + b_bits + 1
|
|
n_rows = 1 << (W - 3) if W > 3 else 1
|
|
mode = cute.ComposedLayout.from_tilelang(_build_swizzled_layout((n_rows, 8), lambda i, j: i * 8 + j, b_bits, m_base, s_shift))
|
|
assert mode is not None and _swizzle(mode) == (b_bits, m_base, s_shift)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"shape,fwd",
|
|
[
|
|
((16, 128), lambda i, j: i * 128 + j), # row-major linear
|
|
((8, 8), lambda i, j: j * 8 + i), # permutation (no XOR)
|
|
((3, 8), lambda i, j: i * 8 + j), # non-pow2 leading dim, linear
|
|
],
|
|
)
|
|
def test_linear_layouts_detected_as_unswizzled(shape, fwd):
|
|
# Linear / permutation layouts are recovered with b_bits == 0 (distinct from
|
|
# the not-detectable None case).
|
|
mode = cute.ComposedLayout.from_tilelang(Layout(shape, fwd))
|
|
assert mode is not None and _swizzle(mode) == (0, 0, 0)
|
|
|
|
|
|
def test_general_pow2_dim_with_nonpow2_high_stride():
|
|
# A pow2-SIZE dim may carry a non-pow2 STRIDE in high bits that is NOT a
|
|
# swizzle source; the detector isolates the inner swizzle and keeps the high
|
|
# strides in the residual layout.
|
|
TILE = 8 * 64
|
|
|
|
def addr(a, b, i, j):
|
|
inner = (j % 8) + ((i % 8) ^ ((j // 8) % 8)) * 8 + i * 64
|
|
return a * (3 * TILE) + b * TILE + inner
|
|
|
|
mode = cute.ComposedLayout.from_tilelang(Layout((2, 3, 8, 64), addr))
|
|
assert mode is not None and _swizzle(mode) == (3, 3, 3)
|
|
strides = list(mode.layout.stride)
|
|
assert 1536 in strides and 512 in strides # outer high strides untouched
|
|
|
|
|
|
def test_nonpow2_leading_size_with_swizzled_inner_tile():
|
|
# A non-pow2 leading SIZE dim is a pass-through; the inner swizzle is still
|
|
# recovered and the batch dim becomes a residual atom.
|
|
def addr(batch, i, j):
|
|
return batch * (8 * 64) + (j % 8) + ((i % 8) ^ ((j // 8) % 8)) * 8 + i * 64
|
|
|
|
mode = cute.ComposedLayout.from_tilelang(Layout((5, 8, 64), addr))
|
|
assert mode is not None and _swizzle(mode) == (3, 3, 3)
|
|
assert 5 in list(mode.layout.shape) and 512 in list(mode.layout.stride)
|
|
|
|
|
|
def test_broadcast_stride_zero_is_recovered():
|
|
# A stride-0 (broadcast) mode is a valid affine layout (8,8):(0,1), not a
|
|
# failure: recovery returns it unswizzled rather than rejecting it.
|
|
mode = cute.ComposedLayout.from_tilelang(Layout((8, 8), lambda i, j: j))
|
|
assert mode is not None and _swizzle(mode) == (0, 0, 0)
|
|
_assert_struct(mode.layout, (8, 8), (0, 1))
|
|
|
|
|
|
def test_nonlinear_not_detectable():
|
|
# A genuinely non-affine map (i*j is not a linear function of the coords)
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# cannot be recovered as a swizzle-over-affine layout, so it returns None.
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assert cute.ComposedLayout.from_tilelang(Layout((8, 8), lambda i, j: i * j)) is None
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|
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def test_non_constant_extent_rejected():
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# A symbolic extent has no fixed-width bit map; ICHECK requires constant.
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n = tvm.tirx.Var("n", "int32")
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with pytest.raises(Exception, match="must be constant"):
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cute.ComposedLayout.from_tilelang(Layout((n, 8), lambda i, j: i * 8 + j))
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|
|
|
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def test_decoder_preserves_input_shape():
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# ComposedLayoutFromTileLang returns a layout congruent to the TileLang input
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# shape (not a flat coalesced layout); a swizzle that splits the contiguous
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# dim yields a hierarchical sub-mode.
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buf = tirx.decl_buffer((64, 512), "float16", name="A", scope="shared")
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mode = cute.ComposedLayout.from_tilelang(make_full_bank_swizzled_layout(buf), buf)
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# Congruent to (64, 512): in byte space the 512-elem contiguous dim is 1024
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# bytes, split by the 128B swizzle atom into (128, 8).
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assert mode.layout.shape == (64, (128, 8))
|
|
|
|
|
|
# ---------------------------------------------------------------------------
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|
# End-to-end TMA copy with an explicitly-written swizzled layout. The forward
|
|
# index gives the design's full-bank (128B) swizzle address:
|
|
# (j % 8) + ((i % 8) ^ ((j // 8) % 8)) * 8 + i * 64 + (j // 64) * 4096
|
|
# ToCuteComposedLayout decodes it to Sw<3,4,3> and LowerBulk drives a swizzled
|
|
# TMA load; the box truncates at the first non-contiguous global mode so the
|
|
# rest replays as 8 unrolled tma_load calls.
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|
# ---------------------------------------------------------------------------
|
|
@tilelang.testing.requires_cuda
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@tilelang.testing.requires_cuda_compute_version_ge(9, 0)
|
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def test_tma_load_with_explicit_swizzled_layout():
|
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import torch
|
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import tilelang
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|
import tilelang.language as T
|
|
|
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M, N = 64, 512
|
|
|
|
def swizzled_addr(i, j):
|
|
return (j % 8) + ((i % 8) ^ ((j // 8) % 8)) * 8 + i * 64 + (j // 64) * 4096
|
|
|
|
@T.prim_func
|
|
def copy_swizzled(X: T.Tensor((M, N), "float16"), ok: T.Tensor((M, N), "int32")):
|
|
with T.Kernel(1, threads=128) as _:
|
|
S = T.alloc_shared((M, N), "float16")
|
|
T.annotate_layout({S: Layout((M, N), swizzled_addr)})
|
|
T.copy(X, S, prefer_instruction="tma")
|
|
# Reading S[i, j] must return the value originally at X[i, j], since
|
|
# annotate_layout changes only physical storage, not the logical map.
|
|
for i, j in T.Parallel(M, N):
|
|
ok[i, j] = T.if_then_else(S[i, j] == T.cast((i * N + j) % 2048, "float16"), 1, 0)
|
|
|
|
buf = tirx.decl_buffer((M, N), "float16", name="S", scope="shared")
|
|
mode = cute.ComposedLayout.from_tilelang(Layout((M, N), swizzled_addr), buf)
|
|
assert mode is not None and _swizzle(mode) == (3, 4, 3)
|
|
|
|
kernel = tilelang.compile(copy_swizzled, out_idx=[1])
|
|
src = kernel.get_kernel_source()
|
|
assert src.count("tma_load(") == 8, "expected 8 (unrolled) TMA loads"
|
|
|
|
ii = torch.arange(M, device="cuda").view(M, 1)
|
|
jj = torch.arange(N, device="cuda").view(1, N)
|
|
X = ((ii * N + jj) % 2048).to(torch.float16)
|
|
ok = kernel(X)
|
|
assert int(ok.min()) == 1, f"{(ok == 0).sum().item()} shared elements mismatched"
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Restrict: affine sub-tile of a layout, one Range per top-level mode. The
|
|
# offset is layout(mins); each mode is reshaped to its logical extent via
|
|
# with_shape, so layout(mins + c) == offset + sublayout(c). A hierarchical
|
|
# (swizzle-split) mode is flattened to the logical slice.
|
|
# ---------------------------------------------------------------------------
|
|
def test_restrict_static_slice():
|
|
from tvm.ir import Range
|
|
|
|
L = cute.make_layout((64, 256), (256, 1))
|
|
off, sub = cute.restrict(L, [Range.from_min_extent(0, 64), Range.from_min_extent(64, 64)])
|
|
assert off == 64
|
|
assert sub.shape == (64, 64)
|
|
assert sub.stride == (256, 1)
|
|
# Identity at an interior coordinate.
|
|
assert L((3, 64 + 5)) == off + sub((3, 5))
|
|
|
|
|
|
def test_restrict_dynamic_slice_origin():
|
|
from tvm.ir import Range
|
|
|
|
j = tvm.tirx.Var("j", "int32")
|
|
L = cute.make_layout((64, 256), (256, 1))
|
|
off, sub = cute.restrict(L, [Range.from_min_extent(0, 64), Range.from_min_extent(j * 64, 64)])
|
|
ana = tvm.arith.Analyzer()
|
|
assert ana.simplify(off - j * 64) == 0
|
|
assert sub.shape == (64, 64)
|
|
# Identity holds symbolically.
|
|
assert ana.simplify(L((3, j * 64 + 5)) - (off + sub((3, 5)))) == 0
|
|
|
|
|
|
def test_restrict_rank_mismatch_rejected():
|
|
from tvm.ir import Range
|
|
|
|
L = cute.make_layout((64, 256), (256, 1))
|
|
with pytest.raises(tvm.error.InternalError):
|
|
cute.restrict(L, [Range.from_min_extent(0, 64)]) # 1 range for a rank-2 layout
|
|
|
|
|
|
def test_restrict_hierarchical_mode():
|
|
from tvm.ir import Range
|
|
|
|
# A hierarchical mode (a swizzle-split K dim, (64,4):(1,4096)) is addressed
|
|
# by a single logical Range; with_shape flattens it to the j-th 64-wide tile.
|
|
j = tvm.tirx.Var("j", "int32")
|
|
L = cute.make_layout((64, (64, 4)), (64, (1, 4096)))
|
|
off, sub = cute.restrict(L, [Range.from_min_extent(0, 64), Range.from_min_extent(j * 64, 64)])
|
|
ana = tvm.arith.Analyzer()
|
|
# Logical K-origin j*64 maps through the split layout to physical j*4096.
|
|
assert ana.simplify(off - j * 4096) == 0
|
|
assert sub.shape == (64, 64)
|
|
# Identity through the hierarchical layout.
|
|
assert ana.simplify(L((3, j * 64 + 5)) - (off + sub((3, 5)))) == 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tilelang.testing.main()
|