Deepseek 4: -sm tensor

ggml/src/ggml-backend-meta.cpp

@@ -592,7 +592,18 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
             GGML_ASSERT(split_states_equal(src_ss[0], src_ss[1]));
             return {assume_sync ? GGML_BACKEND_SPLIT_AXIS_MIRRORED : GGML_BACKEND_SPLIT_AXIS_PARTIAL, {0}, {1}, 1};
         }
-        GGML_ABORT("fatal error");
+        if (src_ss[0].axis == src_ss[1].axis && src_ss[0].axis >= GGML_BACKEND_SPLIT_AXIS_2 &&
+                src_ss[0].axis < GGML_MAX_DIMS) {
+            GGML_ASSERT(split_states_equal(src_ss[0], src_ss[1]));
+            return src_ss[0];
+        }
+        // batched matmul with the batches split across devices and a replicated activation
+        if (src_ss[0].axis >= GGML_BACKEND_SPLIT_AXIS_2 && src_ss[0].axis < GGML_MAX_DIMS &&
+                src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
+            return src_ss[0];
+        }
+        GGML_ABORT("unsupported mul_mat split states: node=%s src0=%s axis=%d src1=%s axis=%d",
+            tensor->name, tensor->src[0]->name, (int) src_ss[0].axis, tensor->src[1]->name, (int) src_ss[1].axis);
         //return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, {1}, 1};
     };
 
@@ -760,14 +771,33 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
     };
 
     auto handle_flash_attn_ext = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
-        GGML_ASSERT(                             src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_2);
-        GGML_ASSERT(                             src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_2);
-        GGML_ASSERT(                             src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_2);
-        GGML_ASSERT(tensor->src[4] == nullptr || src_ss[3].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED);
+        GGML_ASSERT(tensor->src[3] == nullptr || src_ss[3].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED);
+
+        if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
+            GGML_ASSERT(src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED);
+            GGML_ASSERT(src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED);
+            GGML_ASSERT(tensor->src[4] == nullptr || src_ss[4].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED);
+            return {GGML_BACKEND_SPLIT_AXIS_MIRRORED, {0}, {1}, 1};
+        }
+
+        GGML_ASSERT(src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_2);
+        const bool kv_split = src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_2 &&
+                src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_2;
+        const bool kv_mirrored = src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED &&
+                src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED;
+        GGML_ASSERT(kv_split || kv_mirrored);
         GGML_ASSERT(tensor->src[4] == nullptr || src_ss[4].axis == GGML_BACKEND_SPLIT_AXIS_0);
         return {GGML_BACKEND_SPLIT_AXIS_1, {0}, {1}, 1};
     };
 
+    auto handle_lightning_indexer = [&](
+            const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
+        for (size_t i = 0; i < 4; i++) {
+            GGML_ASSERT(src_ss[i].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED);
+        }
+        return {GGML_BACKEND_SPLIT_AXIS_MIRRORED, {0}, {1}, 1};
+    };
+
     auto handle_ssm_conv = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
         if (src_ss[0].axis == src_ss[1].axis) {
             if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_0) {
@@ -938,7 +968,7 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
                 split_state = handle_rope(src_ss);
             } break;
             case GGML_OP_ROPE_BACK: {
-                split_state = handle_generic(src_ss, /*scalar_only =*/ true);
+                split_state = handle_rope(src_ss);
             } break;
             case GGML_OP_CLAMP: {
                 split_state = handle_generic(src_ss, /*scalar_only =*/ false);
@@ -1002,6 +1032,9 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
             case GGML_OP_GATED_DELTA_NET: {
                 split_state = handle_gated_delta_net(src_ss);
             } break;
+            case GGML_OP_LIGHTNING_INDEXER: {
+                split_state = handle_lightning_indexer(src_ss);
+            } break;
             case GGML_OP_DSV4_HC_COMB:
             case GGML_OP_DSV4_HC_PRE:
             case GGML_OP_DSV4_HC_POST: {
@@ -1086,13 +1119,14 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
         if (buf_ctx->debug > 0) {
             std::string srcs_info;
             for (size_t i = 0; i < GGML_MAX_SRC; i++) {
-                if (tensor->src[i] == nullptr) {
+                if (tensor->src[i] == nullptr || tensor->src[i] == tensor) {
                     continue;
                 }
                 if (!srcs_info.empty()) {
                     srcs_info += ", ";
                 }
-                const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor->src[0], true);
+                const ggml_backend_meta_split_state split_state =
+                        ggml_backend_meta_get_split_state(tensor->src[i], true);
                 GGML_ASSERT(split_state.n_segments == 1);
                 const char * axis_name = ggml_backend_meta_split_axis_name(split_state.axis);
                 std::string ne_info;
@@ -1271,6 +1305,108 @@ static enum ggml_status ggml_backend_meta_buffer_init_tensor(ggml_backend_buffer
     return ggml_backend_meta_buffer_init_tensor_impl(buf_ctx->get_simple_tensor_container(tensor), tensor);
 }
 
+static void ggml_backend_meta_buffer_memset_tensor(
+        ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
+    const size_t n_bufs = ggml_backend_meta_buffer_n_bufs(buffer);
+    const ggml_backend_meta_split_state split_state =
+            ggml_backend_meta_get_split_state(tensor, /*assume_sync =*/ false);
+    GGML_ASSERT(ggml_is_contiguous(tensor) || split_state.axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED);
+
+    if (split_state.n_segments != 1 || split_state.nr[0] != 1) {
+        GGML_ASSERT(split_state.axis >= 0 && split_state.axis < GGML_MAX_DIMS);
+        GGML_ASSERT(split_state.nr[0] != 0);
+        GGML_ASSERT(tensor->ne[3] == 1);
+
+        std::vector<size_t> simple_offsets(n_bufs, 0);
+        if (split_state.axis == GGML_BACKEND_SPLIT_AXIS_0) {
+            GGML_ASSERT(tensor->ne[2] == 1);
+
+            const size_t row_stride = tensor->nb[1];
+            GGML_ASSERT(offset % row_stride == 0);
+            GGML_ASSERT(size   % row_stride == 0);
+            const int64_t row_start = offset / row_stride;
+            const int64_t row_count = size   / row_stride;
+            GGML_ASSERT(row_start + row_count <= tensor->ne[1]);
+
+            const int64_t blck_size = ggml_blck_size(tensor->type);
+            for (size_t s = 0; s < split_state.n_segments; s++) {
+                for (size_t r = 0; r < split_state.nr[s]; r++) {
+                    for (size_t j = 0; j < n_bufs; j++) {
+                        ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
+                        GGML_ASSERT(split_state.ne[s*n_bufs + j] % blck_size == 0);
+                        const size_t nbytes = split_state.ne[s*n_bufs + j]/blck_size * tensor->nb[0];
+                        for (int64_t row = 0; row < row_count; row++) {
+                            ggml_backend_tensor_memset(simple_tensor, value,
+                                    simple_offsets[j] + (row_start + row)*simple_tensor->nb[1], nbytes);
+                        }
+                        simple_offsets[j] += nbytes;
+                    }
+                }
+            }
+            return;
+        }
+
+        GGML_ASSERT(split_state.axis == GGML_BACKEND_SPLIT_AXIS_1);
+
+        const size_t row_stride = tensor->nb[2];
+        GGML_ASSERT(offset % row_stride == 0);
+        GGML_ASSERT(size   % row_stride == 0);
+        const int64_t row_start = offset / row_stride;
+        const int64_t row_count = size   / row_stride;
+        GGML_ASSERT(row_start + row_count <= tensor->ne[2]);
+
+        for (size_t s = 0; s < split_state.n_segments; s++) {
+            for (size_t r = 0; r < split_state.nr[s]; r++) {
+                for (size_t j = 0; j < n_bufs; j++) {
+                    ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
+                    const size_t nbytes = split_state.ne[s*n_bufs + j] * tensor->nb[1];
+                    for (int64_t row = 0; row < row_count; row++) {
+                        ggml_backend_tensor_memset(simple_tensor, value,
+                                simple_offsets[j] + (row_start + row)*simple_tensor->nb[2], nbytes);
+                    }
+                    simple_offsets[j] += nbytes;
+                }
+            }
+        }
+        return;
+    }
+
+    switch (split_state.axis) {
+        case GGML_BACKEND_SPLIT_AXIS_0:
+        case GGML_BACKEND_SPLIT_AXIS_1:
+        case GGML_BACKEND_SPLIT_AXIS_2: {
+            const size_t chunk_size_full = tensor->nb[split_state.axis + 1];
+            GGML_ASSERT(offset % chunk_size_full == 0);
+            GGML_ASSERT(size   % chunk_size_full == 0);
+            const int64_t i_start =  offset        / chunk_size_full;
+            const int64_t i_stop  = (offset + size) / chunk_size_full;
+            for (size_t j = 0; j < n_bufs; j++) {
+                ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
+                const size_t chunk_size = simple_tensor->nb[split_state.axis + 1];
+                if (chunk_size == 0) {
+                    continue;
+                }
+                for (int64_t i = i_start; i < i_stop; i++) {
+                    ggml_backend_tensor_memset(simple_tensor, value, i*chunk_size, chunk_size);
+                }
+            }
+        } break;
+        case GGML_BACKEND_SPLIT_AXIS_PARTIAL: {
+            GGML_ASSERT(value == 0);
+            [[fallthrough]];
+        }
+        case GGML_BACKEND_SPLIT_AXIS_MIRRORED: {
+            for (size_t j = 0; j < n_bufs; j++) {
+                ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
+                ggml_backend_tensor_memset(simple_tensor, value, offset, size);
+            }
+        } break;
+        default: {
+            GGML_ABORT("fatal error");
+        }
+    }
+}
+
 static void ggml_backend_meta_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
     const size_t n_bufs = ggml_backend_meta_buffer_n_bufs(buffer);
     const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor, /*assume_sync =*/ false);
@@ -1518,7 +1654,7 @@ static const ggml_backend_buffer_i ggml_backend_meta_buffer_iface = {
     /* .free_buffer     = */ ggml_backend_meta_buffer_free_buffer,
     /* .get_base        = */ ggml_backend_meta_buffer_get_base,
     /* .init_tensor     = */ ggml_backend_meta_buffer_init_tensor,
-    /* .memset_tensor   = */ nullptr, // TODO implement
+    /* .memset_tensor   = */ ggml_backend_meta_buffer_memset_tensor,
     /* .set_tensor      = */ ggml_backend_meta_buffer_set_tensor,
     /* .get_tensor      = */ ggml_backend_meta_buffer_get_tensor,
     /* .set_tensor_2d   = */ nullptr,
@@ -1871,7 +2007,7 @@ static enum ggml_status ggml_backend_meta_graph_compute(ggml_backend_t backend,
 
         {
             // For MoE models it may make sense to delay the AllReduce in order to reduce I/O:
-            auto get_i_delayed = [&](const int i) -> int {
+            auto get_i_delayed_branch = [&](const int i) -> int {
                 int id = i; // i_delayed
                 int idr = i; // i_delayed return, last safe return value
 
@@ -1971,6 +2107,62 @@ static enum ggml_status ggml_backend_meta_graph_compute(ggml_backend_t backend,
                 return idr;
             };
 
+            // AllReduce(a) + AllReduce(b) == AllReduce(a + b) for independent partial branches.
+            auto get_i_delayed = [&](const int i) -> int {
+                const int i_delayed = get_i_delayed_branch(i);
+                ggml_tensor * node = cgraph->nodes[i_delayed];
+
+                if (ggml_node_get_use_count(cgraph, i_delayed) != 1) {
+                    return i_delayed;
+                }
+
+                for (int id = i_delayed + 1; id < cgraph->n_nodes; id++) {
+                    ggml_tensor * next = cgraph->nodes[id];
+                    if (next->view_src == node) {
+                        return i_delayed;
+                    }
+                    for (int s = 0; s < GGML_MAX_SRC; s++) {
+                        if (next->src[s] == node) {
+                            return i_delayed;
+                        }
+                    }
+
+                    if (next->view_src != nullptr && next->view_src->op == GGML_OP_NONE && ggml_backend_buffer_is_host(next->view_src->buffer)) {
+                        continue;
+                    }
+                    if (ggml_backend_meta_get_split_state(next, false).axis != GGML_BACKEND_SPLIT_AXIS_PARTIAL) {
+                        continue;
+                    }
+
+                    const int i_other = id;
+                    const int i_other_delayed = get_i_delayed_branch(i_other);
+                    ggml_tensor * other = cgraph->nodes[i_other_delayed];
+                    if (ggml_node_get_use_count(cgraph, i_other_delayed) != 1 || i_other_delayed + 1 >= cgraph->n_nodes) {
+                        return i_delayed;
+                    }
+
+                    ggml_tensor * sum = cgraph->nodes[i_other_delayed + 1];
+                    if (sum->op != GGML_OP_ADD ||
+                            !ggml_are_same_shape(node, other) || node->type != other->type || sum->type != node->type ||
+                            !((sum->src[0] == node && sum->src[1] == other) ||
+                              (sum->src[0] == other && sum->src[1] == node)) ||
+                            ggml_backend_meta_get_split_state(sum, false).axis != GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
+                        return i_delayed;
+                    }
+
+                    for (size_t j = 0; j < n_backends; j++) {
+                        auto & bcj = backend_ctx->backend_configs[j];
+                        const bool compute       = bcj.nodes[i]->flags       & GGML_TENSOR_FLAG_COMPUTE;
+                        const bool compute_other = bcj.nodes[i_other]->flags & GGML_TENSOR_FLAG_COMPUTE;
+                        if (compute != compute_other) {
+                            return i_delayed;
+                        }
+                    }
+                    return i_other_delayed + 1;
+                }
+                return i_delayed;
+            };
+
             int i_start = 0;
             for (int i = 0; i < cgraph->n_nodes; i++) {
                 ggml_tensor * node = cgraph->nodes[i];

src/llama-arch.cpp

@@ -1060,7 +1060,6 @@ bool llm_arch_supports_sm_tensor(const llm_arch & arch) {
         case LLM_ARCH_OLMOE:
         case LLM_ARCH_DEEPSEEK2:
         case LLM_ARCH_DEEPSEEK32:
-        case LLM_ARCH_DEEPSEEK4:
         case LLM_ARCH_DOTS3NOTE:
         case LLM_ARCH_GLM_DSA:
         case LLM_ARCH_BITNET:

src/llama-kv-cache-dsv4.cpp

@@ -1737,6 +1737,7 @@ void llama_kv_cache_dsv4::clear_compressed(llama_seq_id seq_id, bool data) {
             kv->seq_rm(seq_id, -1, -1);
 
             if (data) {
+                //TODO: do not clear the kv-cache during `seq_rm`, ref: https://github.com/ggml-org/llama.cpp/pull/26490#discussion_r3798143663
                 for (uint32_t il : kv->get_layer_ids()) {
                     dsv4_clear_tensor_stream(kv->get_k_storage(il), (uint32_t) seq_id);
                 }

src/llama-model-saver.cpp

@@ -296,11 +296,18 @@ void llama_model_saver::add_kv_from_model() {
     add_kv(LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT,      hparams.dsv4_o_group_count);
     add_kv(LLM_KV_ATTENTION_OUTPUT_LORA_RANK,        hparams.dsv4_o_lora_rank);
     add_kv(LLM_KV_ATTENTION_COMPRESS_ROPE_FREQ_BASE, hparams.dsv4_compress_rope_base);
-    add_kv(LLM_KV_ATTENTION_COMPRESS_RATIOS,         hparams.dsv4_compress_ratios, true);
-    add_kv(LLM_KV_HYPER_CONNECTION_COUNT,            hparams.dsv4_hc_mult);
+    if (model->arch == LLM_ARCH_DEEPSEEK4 || hparams.dsv4_hc_mult > 0) {
+        // the loader requires one compress ratio per layer, including nextn layers
+        const std::vector<uint32_t> compress_ratios(
+                hparams.dsv4_compress_ratios.begin(), hparams.dsv4_compress_ratios.begin() + hparams.n_layer_all);
+        add_kv(LLM_KV_ATTENTION_COMPRESS_RATIOS,         compress_ratios);
+    } else {
+        add_kv(LLM_KV_ATTENTION_COMPRESS_RATIOS,         hparams.dsv4_compress_ratios, true);
+    }
+    add_kv(LLM_KV_HYPER_CONNECTION_COUNT,               hparams.dsv4_hc_mult);
     add_kv(LLM_KV_HYPER_CONNECTION_SINKHORN_ITERATIONS, hparams.dsv4_hc_sinkhorn_iters);
-    add_kv(LLM_KV_HYPER_CONNECTION_EPSILON,          hparams.dsv4_hc_eps);
-    add_kv(LLM_KV_HASH_LAYER_COUNT,                  hparams.dsv4_hash_layer_count);
+    add_kv(LLM_KV_HYPER_CONNECTION_EPSILON,             hparams.dsv4_hc_eps);
+    add_kv(LLM_KV_HASH_LAYER_COUNT,                     hparams.dsv4_hash_layer_count);
 
     const float rope_scaling_factor = hparams.rope_freq_scale_train == 1.0f ? 0.0f : 1.0f/hparams.rope_freq_scale_train;
 
@@ -425,6 +432,8 @@ void llama_model_saver::add_tensors_from_model() {
     add_tensor(model->output_s);
     add_tensor(model->output_in_s);
     add_tensor(model->output_res_score);
+    add_tensor(model->nextn_proj_pre);
+    add_tensor(model->nextn_proj_post);
     add_tensor(model->cls);
     add_tensor(model->cls_b);
     add_tensor(model->cls_out);