llama : fix buffer checks for mamba and rwk (#10111)

* llama : fix buffer checks for mamba and rwk

* llama : fix missing worst case flag during reserve

* cuda : fix supports_op for norm

* disable sched SET_CAUSE

ggml/src/ggml-backend.cpp

@@ -1508,7 +1508,7 @@ static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, co
     return -1;
 }
 
-#if 1
+#if 0
 #define GGML_SCHED_MAX_SPLITS_DEBUG 4096
 static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_SCHED_MAX_SPLITS_DEBUG*GGML_SCHED_MAX_SPLIT_INPUTS][128]; // debug only
 #define SET_CAUSE(node, ...) sprintf(causes[hash_id(node)], __VA_ARGS__)

ggml/src/ggml-cuda.cu

@@ -3107,18 +3107,20 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                 }
                 return false;
             } break;
+        case GGML_OP_NORM:
+        case GGML_OP_RMS_NORM:
+            return ggml_is_contiguous(op->src[0]) && op->ne[0] % WARP_SIZE == 0;
+            break;
         case GGML_OP_NONE:
         case GGML_OP_RESHAPE:
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
         case GGML_OP_TRANSPOSE:
-        case GGML_OP_NORM:
         case GGML_OP_ADD:
         case GGML_OP_ADD1:
         case GGML_OP_SUB:
         case GGML_OP_MUL:
         case GGML_OP_DIV:
-        case GGML_OP_RMS_NORM:
         case GGML_OP_SCALE:
         case GGML_OP_SQR:
         case GGML_OP_SQRT:

ggml/src/ggml.c

@@ -7272,6 +7272,7 @@ struct ggml_tensor * ggml_ssm_conv(
     const int64_t n_s     = sx->ne[2];
 
     // TODO: maybe support other strides than 1?
+    // FIXME: this is always true?
     GGML_ASSERT(sx->ne[0] == d_conv - 1 + n_t);
     GGML_ASSERT(sx->ne[1] == d_inner);
     GGML_ASSERT(n_t >= 0);

src/llama.cpp

@@ -7127,7 +7127,7 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w
             } break;
         case GGML_OP_MUL_MAT:
             {
-                ggml_tensor * b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, w->ne[0], 512);
+                ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], 512, w->ne[2], w->ne[3]);
                 op_tensor = ggml_mul_mat(ctx, w, b);
             } break;
         case GGML_OP_MUL_MAT_ID:
@@ -7167,18 +7167,38 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w
             } break;
         case GGML_OP_SSM_CONV:
             {
-                // TODO: ggml_ssm_conv(ctx, conv_x, model.layers[il].ssm_conv1d);
+                // FIXME
-                op_tensor = ggml_ssm_conv(ctx, nullptr, w);
+                ggml_tensor * conv_x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 12345, w->ne[1], 6789);
+                op_tensor = ggml_ssm_conv(ctx, conv_x, w);
             } break;
         case GGML_OP_SSM_SCAN:
             {
-                // TODO: ggml_ssm_scan(ctx, ssm, x, dt, model.layers[il].ssm_a, B, C);
+                // FIXME
-                op_tensor = ggml_ssm_scan(ctx, nullptr, nullptr, nullptr, w, nullptr, nullptr);
+                const int64_t d_state      = w->ne[0];
+                const int64_t d_inner      = w->ne[1];
+                const int64_t n_seq_tokens = 512;
+                const int64_t n_seqs       = 1;
+                ggml_tensor * s  = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, d_inner, n_seqs);
+                ggml_tensor * x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs);
+                ggml_tensor * dt = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs);
+                ggml_tensor * B = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs);
+                ggml_tensor * C = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs);
+                op_tensor = ggml_ssm_scan(ctx, s, x, dt, w, B, C);
             } break;
         case GGML_OP_RWKV_WKV:
             {
-                // TODO: ggml_rwkv_wkv(ctx, k, v, r, layer->time_mix_first, w, *wkv_state);
+                // FIXME
-                op_tensor = ggml_rwkv_wkv(ctx, nullptr, nullptr, nullptr, w, nullptr, nullptr);
+                const int64_t S = 123;
+                const int64_t H = 123;
+                const int64_t n_tokens = 123;
+                const int64_t n_seqs = 123;
+                ggml_tensor  * k = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, 1, H, n_tokens);
+                ggml_tensor  * v = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens);
+                ggml_tensor  * r = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens);
+                ggml_tensor  * tf = w;
+                ggml_tensor  * td = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens);
+                ggml_tensor  * state = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, n_seqs, S, H);
+                op_tensor = ggml_rwkv_wkv(ctx, k, v, r, tf, td, state);
             } break;
         default:
             GGML_ABORT("%s: missing test for op %s for tensor %s", __func__, ggml_op_name(op), w->name);
@@ -7453,7 +7473,7 @@ static bool llm_load_tensors(
 
             // tensors with "bias" suffix are always used with GGML_OP_ADD
             ggml_op op;
-            bool bias = strcmp(tn.suffix, "bias") == 0;
+            bool bias = tn.suffix != nullptr && strcmp(tn.suffix, "bias") == 0;
             if (bias) {
                 op = GGML_OP_ADD;
             } else {
@@ -19681,7 +19701,7 @@ struct llama_context * llama_new_context_with_model(
             int n_nodes_tg = ggml_graph_n_nodes(gf_tg);
 
             // reserve again with pp graph to avoid ggml-alloc reallocations during inference
-            gf_pp = llama_build_graph(*ctx, ubatch_pp, false);
+            gf_pp = llama_build_graph(*ctx, ubatch_pp, true);
             if (!ggml_backend_sched_reserve(ctx->sched, gf_pp)) {
                 LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
                 llama_free(ctx);