ver4a/llama.cpp
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vulkan: Implement grouped query attention in the coopmat2 FA shader (#12559)

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When adjacent batches of Q share the same batches of K/V, batch them into
the same workgroup. For example, when:

dst(128,32,1,1) = FA(q(128,1,32,1), k(128,16640,8,1), v(128,16640,8,1))

previously we would run 32 workgroups computing 1 result each, now we will
run 8 workgroups computing 4 results each.

This doesn't directly translate to better performance (at least when you have
>=32 SMs), but in a subsequent change I'll enable split_k which will scale much
better with 4x fewer workgroups.
This commit is contained in:
Jeff Bolz 2025-04-02 12:40:32 -05:00 • committed by GitHub
parent 92e3006bb6
commit be0a0f8cae
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2 changed files with 71 additions and 20 deletions
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66
ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
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@ -61,6 +61,8 @@ layout (push_constant) uniform parameter {
uint32_t n_head_log2;
float m0;
float m1;
uint32_t gqa_ratio;
} p;
layout (binding = 0) readonly buffer Q {uint8_t data_q[];};
@ -103,6 +105,28 @@ ACC_TYPE Max(const in uint32_t row, const in uint32_t col, const in ACC_TYPE ele
#define DECODEFUNC
#endif
// Store the output when doing grouped query attention.
// Rows index by Q's dimension 2, and the first N rows are valid.
D_TYPE perElemOpGqaStore(const in uint32_t r, const in uint32_t c, const in D_TYPE elem, const in uint32_t o_offset, const in uint32_t iq2, const in uint32_t N)
{
if (r < N && c < D) {
uint32_t offset = (iq2 + r) * D + c;
data_o[o_offset + offset] = D_TYPE(elem);
}
return elem;
}
// Load the slope matrix, indexed by Q's dimension 2.
ACC_TYPE perElemOpComputeSlope(const in uint32_t r, const in uint32_t c, const in ACC_TYPE elem, const in uint32_t iq2)
{
const uint32_t h = iq2 + (r & (p.gqa_ratio - 1));
const ACC_TYPE base = ACC_TYPE(h < p.n_head_log2 ? p.m0 : p.m1);
const int exph = int(h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1);
return ACC_TYPE(pow(base, ACC_TYPE(exph)));
}
void main() {
#ifdef NEEDS_INIT_IQ_SHMEM
init_iq_shmem(gl_WorkGroupSize);
@ -116,7 +140,9 @@ void main() {
const uint32_t i = gl_WorkGroupID.x;
const uint32_t iq2 = gl_WorkGroupID.y;
// When not using grouped query attention, all rows share the same iq2, equal to gl_WorkGroupID.y.
// When using grouped query attention, each workgroup does gqa_ratio consecutive values of iq2.
const uint32_t iq2 = gl_WorkGroupID.y * p.gqa_ratio;
const uint32_t iq3 = gl_WorkGroupID.z;
// broadcast factors
@ -149,8 +175,10 @@ void main() {
tensorLayoutK = setTensorLayoutDimensionNV(tensorLayoutK, KV, D);
tensorLayoutV = setTensorLayoutDimensionNV(tensorLayoutV, KV, D);
// nb?1 are already divided by the type size and are in units of elements
uint32_t q_stride = p.nb01;
// nb?1 are already divided by the type size and are in units of elements.
// When using grouped query attention, Q is indexed by iq2, so the stride
// should be nb02 (which is in bytes).
uint32_t q_stride = p.gqa_ratio > 1 ? (p.nb02 / 4) : p.nb01;
uint32_t k_stride = p.nb11;
uint32_t v_stride = p.nb21;
// hint to the compiler that strides are aligned for the aligned variant of the shader
@ -182,16 +210,11 @@ void main() {
L = coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator>(0);
M = coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator>(-1.0/0.0);
ACC_TYPE slope = ACC_TYPE(1.0);
coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> slopeMat = coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator>(1.0);
// ALiBi
if (p.max_bias > 0.0f) {
const uint32_t h = iq2;
const ACC_TYPE base = ACC_TYPE(h < p.n_head_log2 ? p.m0 : p.m1);
const int exph = int(h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1);
slope = pow(base, ACC_TYPE(exph));
coopMatPerElementNV(slopeMat, slopeMat, perElemOpComputeSlope, iq2);
}
[[dont_unroll]]
@ -215,12 +238,16 @@ void main() {
if (p.mask != 0) {
tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutM = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV);
tensorLayoutM = setTensorLayoutDimensionNV(tensorLayoutM, p.nem1, KV);
// When using grouped query attention, all rows use the same mask.
if (p.gqa_ratio > 1) {
tensorLayoutM = setTensorLayoutStrideNV(tensorLayoutM, 0, 1);
}
coopmat<float16_t, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> mv;
coopMatLoadTensorNV(mv, data_m, 0, sliceTensorLayoutNV(tensorLayoutM, i * Br, Br, j * Bc, Bc));
S += slope*coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator>(mv);
S += slopeMat*coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator>(mv);
}
// Clear padding elements to -inf, so they don't contribute to rowmax
@ -297,13 +324,18 @@ void main() {
O = Ldiag*O;
tensorLayoutNV<3, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutD = createTensorLayoutNV(3, gl_CooperativeMatrixClampModeConstantNV);
tensorLayoutD = setTensorLayoutDimensionNV(tensorLayoutD, p.ne2, p.ne1, D);
// permute dimensions
tensorViewNV<3, false, 1, 0, 2> tensorViewPermute = createTensorViewNV(3, false, 1, 0, 2);
uint32_t o_offset = iq3*p.ne2*p.ne1;
coopmat<D_TYPE, gl_ScopeWorkgroup, Br, D, gl_MatrixUseAccumulator> O_D = coopmat<D_TYPE, gl_ScopeWorkgroup, Br, D, gl_MatrixUseAccumulator>(O);
coopMatStoreTensorNV(O_D, data_o, o_offset, sliceTensorLayoutNV(tensorLayoutD, i * Br, Br, iq2, 1, 0, D), tensorViewPermute);
if (p.gqa_ratio > 1) {
coopMatPerElementNV(O_D, O_D, perElemOpGqaStore, o_offset, iq2, N);
} else {
tensorLayoutNV<3, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutD = createTensorLayoutNV(3, gl_CooperativeMatrixClampModeConstantNV);
tensorLayoutD = setTensorLayoutDimensionNV(tensorLayoutD, p.ne2, p.ne1, D);
// permute dimensions
tensorViewNV<3, false, 1, 0, 2> tensorViewPermute = createTensorViewNV(3, false, 1, 0, 2);
coopMatStoreTensorNV(O_D, data_o, o_offset, sliceTensorLayoutNV(tensorLayoutD, i * Br, Br, iq2, N, 0, D), tensorViewPermute);
}
}