Fused delta net 2

ggml/src/ggml-cuda/delta-net.cu

@@ -84,93 +84,75 @@ __global__ void delta_net_recurrent_f32(
     float * sQ = smem;                          // HEAD_DIM
     float * sK = sQ + HEAD_DIM;                 // HEAD_DIM
     float * sV = sK + HEAD_DIM;                 // HEAD_DIM
-    float * sKBeta = sV + HEAD_DIM;             // HEAD_DIM (plain k for state update)
-    float * sVBeta = sKBeta + HEAD_DIM;         // HEAD_DIM (v * sigmoid(beta))
-    float * sOut = sVBeta + HEAD_DIM;           // HEAD_DIM
-    float * sKCumdecay = sOut + HEAD_DIM;       // HEAD_DIM (k * sigmoid(beta) * exp(g))
-    float * sVNew = sKCumdecay + HEAD_DIM;      // HEAD_DIM (v_beta - v_prime)
+    float * sVNew = sV + HEAD_DIM;              // HEAD_DIM
 
     const float scale = rsqrtf((float)HEAD_DIM);
 
     __shared__ float sum_helper[block_size/WARP_SIZE];
 
     // Copy initial state to output buffer (will be updated in place)
-    for (int i = tid; i < HEAD_DIM * HEAD_DIM; i += blockDim.x) {
+    for (int i = tid; i < HEAD_DIM * HEAD_DIM; i += block_size) {
         state_dst[i] = state_src[i];
     }
-    __syncthreads();
+
+    constexpr int HEAD_DIM_S = HEAD_DIM + 1;
+    __shared__ float all_sum[2*HEAD_DIM_S*NUM_WARPS];
+    auto all_sum1 = all_sum;
+    auto all_sum2 = all_sum1 + HEAD_DIM_S*NUM_WARPS;
 
     // Process each token sequentially
     for (int64_t t = 0; t < n_tokens; t++) {
 
-        float q_sq = 0.0f;
-        float k_sq = 0.0f;
-        for (int i = tid; i < HEAD_DIM; i += blockDim.x) {
-            sQ[i] = q_ptr[t * qkv_stride_token + i];
+        float sum_kq = 0.0f;
+        for (int i = tid; i < HEAD_DIM; i += block_size) {
+            sQ[i] = q_ptr[t * qkv_stride_token + i] * scale;
             sK[i] = k_ptr[t * qkv_stride_token + i];
             sV[i] = v_ptr[t * qkv_stride_token + i];
-            q_sq += sQ[i] * sQ[i];
-            k_sq += sK[i] * sK[i];
+            sum_kq += sK[i] * sQ[i];
         }
 
-        q_sq = reduce_sum<block_size>(q_sq, sum_helper);
-        k_sq = reduce_sum<block_size>(k_sq, sum_helper);
-
-        float q_norm = rsqrtf(q_sq + eps);
-        float k_norm = rsqrtf(k_sq + eps);
+        float attn_score = reduce_sum<block_size>(sum_kq, sum_helper);
 
         float beta_val = sigmoid_f(beta_ptr[t]);
         float decay    = expf(fminf(g_ptr[t], 50.0f));
 
-        float sum = 0;
-        for (int i = tid; i < HEAD_DIM; i += blockDim.x) {
-            sQ[i] = sQ[i] * q_norm * scale;
-            sK[i] = sK[i] * k_norm;
-            sKBeta[i] = sK[i];
-            sVBeta[i] = sV[i] * beta_val;
-            sKCumdecay[i] = sK[i] * beta_val * decay;
-            sum += sK[i] * sQ[i];
-        }
-        float attn_score = reduce_sum<block_size>(sum, sum_helper);
-        //__syncthreads();
-
-        for (int row_out = warp_id; row_out < HEAD_DIM; row_out += NUM_WARPS) {
+        for (int row_out = lane_id; row_out < HEAD_DIM; row_out += WARP_SIZE) {
             float sum1 = 0.0f;
             float sum2 = 0.0f;
             #pragma unroll
-            for (int col = lane_id; col < HEAD_DIM; col += WARP_SIZE) {
+            for (int col = warp_id; col < HEAD_DIM; col += NUM_WARPS) {
                 float sval = state_dst[row_out + col * HEAD_DIM];
-                sum1 += sval * sKCumdecay[col];
+                sum1 += sval * sK[col];
                 sum2 += sval * sQ[col];
             }
-            sum1 = warp_reduce_sum(sum1);
-            sum2 = warp_reduce_sum(sum2);
-            if (lane_id == 0) {
-                sVNew[row_out] = sVBeta[row_out] - sum1;
-                float v_attn = sVNew[row_out] * attn_score;
-                //sOut[row_out] = sum2 * decay + v_attn;
-                out_base[t * out_token_stride + row_out] = sum2 * decay + v_attn;
+            all_sum1[warp_id*HEAD_DIM_S + row_out] = sum1;
+            all_sum2[warp_id*HEAD_DIM_S + row_out] = sum2;
+        }
+        __syncthreads();
+
+        for (int row_out = tid; row_out < HEAD_DIM; row_out += block_size) {
+            float sum1 = all_sum1[row_out];
+            float sum2 = all_sum2[row_out];
+            #pragma unroll
+            for (int i = 1; i < NUM_WARPS; ++i) {
+                sum1 += all_sum1[row_out + i*HEAD_DIM_S];
+                sum2 += all_sum2[row_out + i*HEAD_DIM_S];
             }
+            sVNew[row_out] = sV[row_out] * beta_val - sum1 * beta_val * decay;
+            float v_attn = sVNew[row_out] * attn_score;
+            out_base[t * out_token_stride + row_out] = sum2 * decay + v_attn;
         }
         __syncthreads();
 
         for (int out_dim = warp_id; out_dim < HEAD_DIM; out_dim += NUM_WARPS) {
             #pragma unroll
             for (int row = lane_id; row < HEAD_DIM; row += WARP_SIZE) {
                 float state_val = state_dst[row + out_dim * HEAD_DIM];
-                float safe_decay = decay;
-                if (isnan(safe_decay) || isinf(safe_decay)) {
-                    safe_decay = 1.0f;
-                }
-                float new_state_val = safe_decay * state_val + sVNew[row] * sKBeta[out_dim];
+                float new_state_val = decay * state_val + sVNew[row] * sK[out_dim];
                 new_state_val = fminf(fmaxf(new_state_val, -1e6f), 1e6f);
                 state_dst[row + out_dim * HEAD_DIM] = new_state_val;
             }
         }
-        if (t < n_tokens - 1) {
-            __syncthreads();
-        }
-
     }
 }
 
@@ -408,9 +390,7 @@ static void delta_net_f32_cuda(
     const int num_blocks = n_seqs * n_heads;
     constexpr int threads_per_block = 512; //256;
 
-    // Shared memory: 9 * head_dim (for Q, K, V, KBeta, VBeta, Out, KCumdecay, VPrime, VNew)
-    // Plus 6 floats for Norm[2], g_val, beta_val, decay, attn_score
-    const size_t smem_size = (9 * head_dim + 6) * sizeof(float);
+    const size_t smem_size = 4 * head_dim * sizeof(float);
 
     // Use templated kernel for common head dimensions, generic for others
     if (head_dim == 64) {
@@ -421,6 +401,7 @@ static void delta_net_f32_cuda(
         delta_net_recurrent_f32<128, threads_per_block><<<num_blocks, threads_per_block, smem_size, stream>>>(
                     q, k, v, g, beta, state_in, dst, n_heads, n_tokens, n_seqs, output_offset, eps);
     } else {
+        GGML_ASSERT("Unsupported delta net head size");
         delta_net_recurrent_generic_f32<<<num_blocks, threads_per_block, smem_size, stream>>>(
             q, k, v, g, beta, state_in, dst, head_dim, n_tokens, n_heads, n_seqs, output_offset, eps);
     }

ggml/src/iqk/iqk_mul_mat.cpp

@@ -1397,7 +1397,6 @@ void iqk_fused_delta_net_impl(int n_heads, int n_tokens, int n_seqs,
     static_assert(head_dim % 8 == 0);
 #endif
 
-    const float eps = 1e-6f;
     const float scale = 1.0f / sqrtf((float) head_dim);
 
     float v_new_buf[head_dim];
@@ -1428,42 +1427,25 @@ void iqk_fused_delta_net_impl(int n_heads, int n_tokens, int n_seqs,
             const float g_val    = g_data[g_head_offset + t];
             const float beta_raw = beta_data[g_head_offset + t];
 
-            float q_norm_sq = 0.0f;
-            float k_norm_sq = 0.0f;
             float kq_sum    = 0.0f;
 #ifdef __AVX2__
-            auto vqsum  = _mm256_setzero_ps();
-            auto vksum  = _mm256_setzero_ps();
             auto vqksum = _mm256_setzero_ps();
             for (int i = 0; i < head_dim; i += 8) {
                 auto vq = _mm256_loadu_ps(q_t + i);
                 auto vk = _mm256_loadu_ps(k_t + i);
-                vqsum  = _mm256_fmadd_ps(vq, vq, vqsum);
-                vksum  = _mm256_fmadd_ps(vk, vk, vksum);
                 vqksum = _mm256_fmadd_ps(vk, vq, vqksum);
             }
-            q_norm_sq = hsum_float_8(vqsum);
-            k_norm_sq = hsum_float_8(vksum);
-            kq_sum    = hsum_float_8(vqksum);
+            kq_sum = hsum_float_8(vqksum);
 #else
             for (int i = 0; i < head_dim; ++i) {
-                q_norm_sq += q_t[i] * q_t[i];
-                k_norm_sq += k_t[i] * k_t[i];
-                kq_sum    += k_t[i] * q_t[i];
+                kq_sum += k_t[i] * q_t[i];
             }
 #endif
-            const float q_norm_inv = 1.0f / sqrtf(q_norm_sq + eps);
-            const float k_norm_inv = 1.0f / sqrtf(k_norm_sq + eps);
 
             const float beta_val = 1.0f / (1.0f + expf(-beta_raw));
             const float decay    = expf(fminf(g_val, 50.0f));
 
-            float attn_score = kq_sum * k_norm_inv * q_norm_inv * scale;
-
-            //float attn_score = 0.0f;
-            //for (int i = 0; i < head_dim; ++i) {
-            //    attn_score += (k_t[i] * k_norm_inv) * (q_t[i] * q_norm_inv * scale);
-            //}
+            float attn_score = kq_sum * scale;
 
             float * out_t = out_data + out_head_offset + t * out_token_stride;
 
@@ -1479,17 +1461,17 @@ void iqk_fused_delta_net_impl(int n_heads, int n_tokens, int n_seqs,
                 }
             }
             for (int row = 0; row < head_dim; ++row) {
-                const float v_new = v_t[row] * beta_val - v_prime[row] * beta_val * decay * k_norm_inv;
+                const float v_new = v_t[row] * beta_val - v_prime[row] * beta_val * decay;
                 v_new_buf[row] = v_new;
-                out_t[row] = out_val[row] * decay * q_norm_inv * scale + v_new * attn_score;
+                out_t[row] = out_val[row] * decay * scale + v_new * attn_score;
             }
 
 #ifdef __AVX2__
             auto vd = _mm256_set1_ps(decay);
             auto vmin = _mm256_set1_ps(-1e6f);
             auto vmax = _mm256_set1_ps( 1e6f);
             for (int col = 0; col < head_dim; ++col) {
-                auto vk = _mm256_set1_ps(k_t[col] * k_norm_inv);
+                auto vk = _mm256_set1_ps(k_t[col]);
                 for (int row = 0; row < head_dim; row += 8) {
                     auto vs = _mm256_loadu_ps(state + col * head_dim + row);
                     auto vn = _mm256_loadu_ps(v_new_buf + row);
@@ -1503,7 +1485,7 @@ void iqk_fused_delta_net_impl(int n_heads, int n_tokens, int n_seqs,
             }
 #else
             for (int col = 0; col < head_dim; ++col) {
-                const float k_col = k_t[col] * k_norm_inv;
+                const float k_col = k_t[col];
                 for (int row = 0; row < head_dim; ++row) {
                     float s = state[row + col * head_dim];
                     s = decay * s + v_new_buf[row] * k_col;

src/llama.cpp

@@ -2209,7 +2209,7 @@ static bool llm_load_tensors(
 
     // print memory requirements
     for (ggml_backend_buffer_t buf : model.bufs) {
-        LLAMA_LOG_DEBUG("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0);
+        LLAMA_LOG_INFO("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0);
     }
 
     // populate tensors_by_name