ggml : fix FA mask dim 2 and 3 (#14505)
* ggml : fix FA mask dim 2 and 3 ggml-ci * backends : unsupport batched FA in CUDA and Vulkan ggml-ci * vulkan : disable FA for mask->ne[2] != 1
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@ -1983,15 +1983,16 @@ extern "C" {
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#define GGML_KQ_MASK_PAD 64
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// q: [n_embd_k, n_batch, n_head, ne3]
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// k: [n_embd_k, n_kv, n_head_kv, ne3]
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// v: [n_embd_v, n_kv, n_head_kv, ne3] !! not transposed !!
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// mask: [n_kv, n_batch_pad, ne32, 1] !! n_batch_pad = GGML_PAD(n_batch, GGML_KQ_MASK_PAD) !!
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// res: [n_embd_v, n_head, n_batch, ne3] !! permuted !!
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// q: [n_embd_k, n_batch, n_head, ne3 ]
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// k: [n_embd_k, n_kv, n_head_kv, ne3 ]
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// v: [n_embd_v, n_kv, n_head_kv, ne3 ] !! not transposed !!
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// mask: [n_kv, n_batch_pad, ne32, ne33] !! n_batch_pad = GGML_PAD(n_batch, GGML_KQ_MASK_PAD) !!
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// res: [n_embd_v, n_head, n_batch, ne3 ] !! permuted !!
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//
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// broadcast:
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// n_head % n_head_kv == 0
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// ne3 % ne32 == 0
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// n_head % ne32 == 0
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// ne3 % ne33 == 0
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//
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GGML_API struct ggml_tensor * ggml_flash_attn_ext(
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struct ggml_context * ctx,
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@ -7799,7 +7799,7 @@ static void ggml_compute_forward_flash_attn_ext_f16(
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memset(VKQ32, 0, DV*sizeof(float));
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}
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const ggml_fp16_t * mp = mask ? (ggml_fp16_t *)((char *) mask->data + iq1*mask->nb[1] + (iq3%mask->ne[2])*mask->nb[2]) : NULL;
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const ggml_fp16_t * mp = mask ? (ggml_fp16_t *)((char *) mask->data + iq1*mask->nb[1] + (iq2%mask->ne[2])*mask->nb[2] + (iq3%mask->ne[3])*mask->nb[3]) : NULL;
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// k indices
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const int ik3 = iq3 / rk3;
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@ -3390,7 +3390,8 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
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return false;
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}
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// TODO: support broadcast
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// ref: https://github.com/ggml-org/llama.cpp/pull/14435
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// note: this was initially implemented in https://github.com/ggml-org/llama.cpp/pull/14500, but
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// the interface of ggml_flash_attn_ext() changed in https://github.com/ggml-org/llama.cpp/pull/14505
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if (op->src[0]->ne[3] != 1) {
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return false;
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}
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@ -230,8 +230,10 @@ typedef struct {
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uint64_t nb22;
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uint64_t nb23;
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int32_t ne32;
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int32_t ne33;
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uint64_t nb31;
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uint64_t nb32;
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uint64_t nb33;
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int32_t ne1;
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int32_t ne2;
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float scale;
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@ -5018,8 +5018,10 @@ static bool ggml_metal_encode_node(
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/*.nb22 =*/ nb22,
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/*.nb23 =*/ nb23,
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/*.ne32 =*/ ne32,
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/*.ne33 =*/ ne33,
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/*.nb31 =*/ nb31,
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/*.nb32 =*/ nb32,
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/*.nb33 =*/ nb33,
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/*.ne1 =*/ ne1,
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/*.ne2 =*/ ne2,
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/*.scale =*/ scale,
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@ -3857,7 +3857,7 @@ kernel void kernel_flash_attn_ext(
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// load the mask in shared memory
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#pragma unroll(Q)
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for (short j = 0; j < Q; ++j) {
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device const half * pm = (device const half *) ((device const char *) mask + (iq1 + j)*args.nb31 + (iq3%args.ne32)*args.nb32);
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device const half * pm = (device const half *) ((device const char *) mask + (iq1 + j)*args.nb31 + (iq2%args.ne32)*args.nb32 + (iq3%args.ne33)*args.nb33);
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const float m = pm[ic + tiisg];
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@ -4343,7 +4343,7 @@ kernel void kernel_flash_attn_ext_vec(
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const bool has_mask = mask != q;
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// pointer to the mask
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device const half * pm = (device const half *) (mask + iq1*args.nb31 + (iq3%args.ne32)*args.nb32);
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device const half * pm = (device const half *) (mask + iq1*args.nb31 + (iq2%args.ne32)*args.nb32 + (iq3%args.ne33)*args.nb33);
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float slope = 1.0f;
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@ -10265,6 +10265,12 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
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if (op->src[3] && op->src[3]->type != GGML_TYPE_F16) {
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return false;
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}
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// TODO: support broadcast
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// note: this was initially implemented in https://github.com/ggml-org/llama.cpp/pull/14449, but
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// the interface of ggml_flash_attn_ext() changed in https://github.com/ggml-org/llama.cpp/pull/14505
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if (op->src[0]->ne[3] != 1 || (op->src[3] && op->src[3]->ne[2] != 1)) {
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return false;
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}
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// It's straightforward to support different K/V dequant, but would
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// significantly increase the number of pipelines
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if (op->src[1]->type != op->src[2]->type) {
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@ -3674,7 +3674,6 @@ static struct ggml_tensor * ggml_soft_max_impl(
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if (mask) {
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GGML_ASSERT(mask->type == GGML_TYPE_F16 || mask->type == GGML_TYPE_F32);
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GGML_ASSERT(ggml_is_contiguous(mask));
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GGML_ASSERT(ggml_is_3d(mask));
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GGML_ASSERT(mask->ne[0] == a->ne[0]);
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GGML_ASSERT(mask->ne[1] >= a->ne[1]);
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GGML_ASSERT(a->ne[2]%mask->ne[2] == 0);
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@ -4704,12 +4703,12 @@ struct ggml_tensor * ggml_flash_attn_ext(
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if (mask) {
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GGML_ASSERT(ggml_is_contiguous(mask));
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GGML_ASSERT(mask->ne[2] == q->ne[3]);
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GGML_ASSERT(mask->ne[1] >= GGML_PAD(q->ne[1], GGML_KQ_MASK_PAD) &&
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"the Flash-Attention kernel requires the mask to be padded to GGML_KQ_MASK_PAD and at least n_queries big");
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//GGML_ASSERT(ggml_can_repeat_rows(mask, qk));
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GGML_ASSERT(q->ne[3] % mask->ne[2] == 0);
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GGML_ASSERT(q->ne[2] % mask->ne[2] == 0);
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GGML_ASSERT(q->ne[3] % mask->ne[3] == 0);
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}
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if (max_bias > 0.0f) {
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@ -3637,7 +3637,7 @@ struct test_flash_attn_ext : public test_case {
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ggml_tensor * m = nullptr;
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if (mask) {
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m = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, GGML_PAD(nb, GGML_KQ_MASK_PAD), nr23[1], 1);
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m = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, GGML_PAD(nb, GGML_KQ_MASK_PAD), nr23[0], nr23[1]);
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ggml_set_name(m, "m");
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}
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@ -4751,7 +4751,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
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test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0-1, ne1-1, 1, 1}, mask, m_prec, {1, 1}, scale, max_bias));
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if (ne0 <= 32 && ne1 <= 32) {
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test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1}, mask, m_prec, {3, 1}, scale, max_bias));
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test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 3}, mask, m_prec, {3, 1}, scale, max_bias));
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test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0-1, ne1-1, 1, 1}, mask, m_prec, {2, 3}, scale, max_bias));
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}
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}
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