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vulkan: Implement grouped query attention in the coopmat2 FA shader #12559

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Apr 2, 2025
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vulkan: Implement grouped query attention in the coopmat2 FA shader #12559

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25 changes: 22 additions & 3 deletions ggml/src/ggml-vulkan/ggml-vulkan.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,7 @@

#define ROUNDUP_POW2(M, N) (((M) + (N) - 1) & ~((N) - 1))
#define CEIL_DIV(M, N) (((M) + (N)-1) / (N))
static bool is_pow2(uint32_t x) { return x > 1 && (x & (x-1)) == 0; }

#define VK_VENDOR_ID_AMD 0x1002
#define VK_VENDOR_ID_APPLE 0x106b
Expand Down Expand Up @@ -490,6 +491,8 @@ struct vk_flash_attn_push_constants {
uint32_t n_head_log2;
float m0;
float m1;

uint32_t gqa_ratio;
};

struct vk_op_push_constants {
Expand Down Expand Up @@ -5232,7 +5235,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
const uint32_t nbm1 = mask ? mask->nb[1] : 0;

const uint32_t D = neq0;
const uint32_t N = neq1;
uint32_t N = neq1;
const uint32_t KV = nek1;

GGML_ASSERT(ne0 == D);
Expand Down Expand Up @@ -5290,6 +5293,22 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
vk_pipeline pipeline = pipelines[aligned];
assert(pipeline);

uint32_t gqa_ratio = 1;
uint32_t qk_ratio = neq2 / nek2;
uint32_t workgroups_x = (uint32_t)neq1;
uint32_t workgroups_y = (uint32_t)neq2;
uint32_t workgroups_z = (uint32_t)neq3;

if (N == 1 && qk_ratio > 1 && is_pow2(qk_ratio) && gqa_ratio <= flash_attention_num_small_rows &&
qk_ratio * nek2 == neq2 && nek2 == nev2 && neq3 == 1 && nek3 == 1 && nev3 == 1) {
// grouped query attention - make the N dimension equal to gqa_ratio, reduce
// workgroups proportionally in y dimension. The shader will detect gqa_ratio > 1
// and change addressing calculations to index Q's dimension 2.
gqa_ratio = qk_ratio;
N = gqa_ratio;
workgroups_y /= N;
}

if (dryrun) {
// Request descriptor sets
ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1);
Expand Down Expand Up @@ -5379,7 +5398,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
v_stride, (uint32_t)nbv2, (uint32_t)nbv3,
nbm1,
scale, max_bias, logit_softcap,
mask != nullptr, n_head_log2, m0, m1 };
mask != nullptr, n_head_log2, m0, m1, gqa_ratio };
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
{
vk_subbuffer{d_Q, q_buf_offset, VK_WHOLE_SIZE},
Expand All @@ -5388,7 +5407,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
vk_subbuffer{d_M, m_buf_offset, VK_WHOLE_SIZE},
vk_subbuffer{d_D, d_buf_offset, VK_WHOLE_SIZE},
},
sizeof(vk_flash_attn_push_constants), &pc, { (uint32_t)neq1, (uint32_t)neq2, (uint32_t)neq3 });
sizeof(vk_flash_attn_push_constants), &pc, { workgroups_x, workgroups_y, workgroups_z });
}

static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op) {
Expand Down
66 changes: 49 additions & 17 deletions ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp
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Original file line number Diff line number Diff line change
Expand Up @@ -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[];};
Expand Down Expand Up @@ -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);
Expand All @@ -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
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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]]
Expand All @@ -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
Expand Down Expand Up @@ -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);
}
}
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