ikawrakow/ik_llama.cpp
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Remove unused stuff

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Kawrakow
2026-02-27 09:47:17 +00:00
parent 745dee7d4e
commit e2dbf3acc3
1 changed files with 0 additions and 496 deletions
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ggml/src/ggml-cuda/delta-net.cu
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@@ -157,469 +157,6 @@ __global__ void delta_net_recurrent_f32_a(
}
}
template <int HEAD_DIM, int block_size>
__global__ void delta_net_recurrent_f32(
const float * __restrict__ q, // [HEAD_DIM, n_tokens, n_heads, n_seqs]
const float * __restrict__ k, // [HEAD_DIM, n_tokens, n_heads, n_seqs]
const float * __restrict__ v, // [HEAD_DIM, n_tokens, n_heads, n_seqs]
const float * __restrict__ g, // [n_tokens, 1, n_heads, n_seqs]
const float * __restrict__ beta_in, // [1, n_tokens, n_heads, n_seqs]
const float * __restrict__ state_in, // [HEAD_DIM, HEAD_DIM*n_heads, 1, n_seqs]
float * __restrict__ dst, // output + new_state concatenated
const int64_t n_heads,
const int64_t n_tokens,
const int64_t n_seqs,
const int64_t output_offset, // offset where state starts in output
const float eps) {
const int batch_idx = blockIdx.x / n_heads;
const int head_idx = blockIdx.x % n_heads;
const int tid = threadIdx.x;
const int warp_id = tid / WARP_SIZE; // 0-7 for 256 threads
const int lane_id = tid % WARP_SIZE; // 0-31
constexpr int NUM_WARPS = block_size/WARP_SIZE;
// Strides for input tensors (column-major)
// Q/K/V: [HEAD_DIM, n_tokens, n_heads, n_seqs]
const int64_t qkv_stride_token = HEAD_DIM;
const int64_t qkv_stride_head = HEAD_DIM * n_tokens;
const int64_t qkv_stride_batch = HEAD_DIM * n_tokens * n_heads;
// G/Beta: [n_tokens, 1, n_heads, n_seqs] / [1, n_tokens, n_heads, n_seqs]
const int64_t g_stride_head = n_tokens;
const int64_t g_stride_batch = n_tokens * n_heads;
// State: [HEAD_DIM, HEAD_DIM*n_heads, 1, n_seqs]
// For head h: columns h*HEAD_DIM to (h+1)*HEAD_DIM
// state[row, col] for head h = state[row, h*HEAD_DIM + col]
// Linear index: row + (h*HEAD_DIM + col) * HEAD_DIM = row + h*HEAD_DIM^2 + col*HEAD_DIM
const int64_t state_head_offset = head_idx * HEAD_DIM * HEAD_DIM;
const int64_t state_batch_stride = HEAD_DIM * HEAD_DIM * n_heads;
// Pointers for this batch/head
const float * q_ptr = q + batch_idx * qkv_stride_batch + head_idx * qkv_stride_head;
const float * k_ptr = k + batch_idx * qkv_stride_batch + head_idx * qkv_stride_head;
const float * v_ptr = v + batch_idx * qkv_stride_batch + head_idx * qkv_stride_head;
const float * g_ptr = g + batch_idx * g_stride_batch + head_idx * g_stride_head;
const float * beta_ptr = beta_in + batch_idx * g_stride_batch + head_idx * g_stride_head;
const float * state_src = state_in + batch_idx * state_batch_stride + state_head_offset;
// Output layout: [head_v_dim, num_v_heads, n_seq_tokens, n_seqs]
// For [dim, head, token, batch]: index = dim + head*S_v + token*S_v*H_v + batch*S_v*H_v*n_tokens
float * out_base = dst + batch_idx * (HEAD_DIM * n_heads * n_tokens) + head_idx * HEAD_DIM;
const int64_t out_token_stride = HEAD_DIM * n_heads; // stride between tokens
float * state_dst = dst + output_offset + batch_idx * state_batch_stride + state_head_offset;
// Shared memory for current token's Q, K, V (normalized), and intermediate results
extern __shared__ float smem[];
float * sQ = smem; // HEAD_DIM
float * sK = sQ + HEAD_DIM; // HEAD_DIM
float * sV = sK + HEAD_DIM; // HEAD_DIM
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 += block_size) {
state_dst[i] = state_src[i];
}
constexpr int HEAD_DIM_S = HEAD_DIM + 1;
constexpr int num_stored_rows = block_size >= HEAD_DIM && block_size % HEAD_DIM == 0 ? block_size/HEAD_DIM : NUM_WARPS;
__shared__ float all_sum[2*HEAD_DIM_S*num_stored_rows];
auto all_sum1 = all_sum;
auto all_sum2 = all_sum1 + HEAD_DIM_S*num_stored_rows;
if constexpr (block_size >= HEAD_DIM && block_size % HEAD_DIM == 0) {
int idx = tid / HEAD_DIM;
int row_out = tid % HEAD_DIM;
for (int64_t t = 0; t < n_tokens; t++) {
if (idx == 0) {
sQ[row_out] = q_ptr[t * qkv_stride_token + row_out] * scale;
sK[row_out] = k_ptr[t * qkv_stride_token + row_out];
float kq = sQ[row_out]*sK[row_out];
kq = warp_reduce_sum(kq);
if (row_out % WARP_SIZE == 0) {
sum_helper[row_out/WARP_SIZE] = kq;
}
}
__syncthreads();
float attn_score = 0;
for (int i = 0; i < HEAD_DIM/WARP_SIZE; ++i) {
attn_score += sum_helper[i];
}
float beta_val = sigmoid_f(beta_ptr[t]);
float decay = expf(fminf(g_ptr[t], 50.0f));
float sum1 = 0, sum2 = 0;
#pragma unroll
for (int col = idx; col < HEAD_DIM; col += block_size/HEAD_DIM) {
float sval = state_dst[row_out + col * HEAD_DIM];
sum1 += sval * sK[col];
sum2 += sval * sQ[col];
}
all_sum1[idx*HEAD_DIM_S + row_out] = sum1;
all_sum2[idx*HEAD_DIM_S + row_out] = sum2;
__syncthreads();
sum1 = sum2 = 0;
#pragma unroll
for (int i = 0; i < block_size/HEAD_DIM; ++i) {
sum1 += all_sum1[i*HEAD_DIM_S + row_out];
sum2 += all_sum2[i*HEAD_DIM_S + row_out];
}
float sv_new = beta_val * (v_ptr[t * qkv_stride_token + row_out] - sum1 * decay);
if (idx == 0) {
out_base[t * out_token_stride + row_out] = sum2 * decay + sv_new * attn_score;
}
for (int col = idx; col < HEAD_DIM; col += block_size/HEAD_DIM) {
float state_val = state_dst[row_out + col * HEAD_DIM];
float new_state_val = decay * state_val + sv_new * sK[col];
new_state_val = fminf(fmaxf(new_state_val, -1e6f), 1e6f);
state_dst[row_out + col * HEAD_DIM] = new_state_val;
}
}
} else {
// Process each token sequentially
for (int64_t t = 0; t < n_tokens; t++) {
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];
sum_kq += sK[i] * sQ[i];
}
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));
//if constexpr (block_size >= HEAD_DIM && block_size % HEAD_DIM == 0) {
// int idx = tid / HEAD_DIM;
// int row_out = tid % HEAD_DIM;
// float sum1 = 0, sum2 = 0;
// #pragma unroll
// for (int col = idx; col < HEAD_DIM; col += block_size/HEAD_DIM) {
// float sval = state_dst[row_out + col * HEAD_DIM];
// sum1 += sval * sK[col];
// sum2 += sval * sQ[col];
// }
// all_sum1[idx*HEAD_DIM_S + row_out] = sum1;
// all_sum2[idx*HEAD_DIM_S + row_out] = sum2;
// __syncthreads();
// sum1 = sum2 = 0;
// #pragma unroll
// for (int i = 0; i < block_size/HEAD_DIM; ++i) {
// sum1 += all_sum1[i*HEAD_DIM_S + row_out];
// sum2 += all_sum2[i*HEAD_DIM_S + row_out];
// }
// float sv_new = sV[row_out] * beta_val - sum1 * beta_val * decay;
// if (idx == 0) {
// out_base[t * out_token_stride + row_out] = sum2 * decay + sv_new * attn_score;
// }
// for (int col = idx; col < HEAD_DIM; col += block_size/HEAD_DIM) {
// float state_val = state_dst[row_out + col * HEAD_DIM];
// float new_state_val = decay * state_val + sv_new * sK[col];
// new_state_val = fminf(fmaxf(new_state_val, -1e6f), 1e6f);
// state_dst[row_out + col * HEAD_DIM] = new_state_val;
// }
// //if (idx == 0) {
// // #pragma unroll
// // for (int i = 1; i < block_size/HEAD_DIM; ++i) {
// // sum1 += all_sum1[i*HEAD_DIM_S + row_out];
// // sum2 += all_sum2[i*HEAD_DIM_S + row_out];
// // }
// // 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 col = idx; col < HEAD_DIM; col += block_size/HEAD_DIM) {
// // float state_val = state_dst[row_out + col * HEAD_DIM];
// // float new_state_val = decay * state_val + sVNew[row_out] * sK[col];
// // new_state_val = fminf(fmaxf(new_state_val, -1e6f), 1e6f);
// // state_dst[row_out + col * HEAD_DIM] = new_state_val;
// //}
// sum1 = sum2 = 0;
// #pragma unroll
// for (int i = 0; i < block_size/HEAD_DIM; ++i) {
// sum1 += all_sum1[i*HEAD_DIM_S + row_out];
// sum2 += all_sum2[i*HEAD_DIM_S + row_out];
// }
// float sv_new = sV[row_out] * beta_val - sum1 * beta_val * decay;
// if (idx == 0) {
// out_base[t * out_token_stride + row_out] = sum2 * decay + sv_new * attn_score;
// }
// for (int col = idx; col < HEAD_DIM; col += block_size/HEAD_DIM) {
// float state_val = state_dst[row_out + col * HEAD_DIM];
// float new_state_val = decay * state_val + sv_new * sK[col];
// new_state_val = fminf(fmaxf(new_state_val, -1e6f), 1e6f);
// state_dst[row_out + col * HEAD_DIM] = new_state_val;
// }
//} else {
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 = warp_id; col < HEAD_DIM; col += NUM_WARPS) {
float sval = state_dst[row_out + col * HEAD_DIM];
sum1 += sval * sK[col];
sum2 += sval * sQ[col];
}
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) {
float k_col = sK[out_dim];
#pragma unroll
for (int row = lane_id; row < HEAD_DIM; row += WARP_SIZE) {
float state_val = state_dst[row + out_dim * HEAD_DIM];
float new_state_val = decay * state_val + sVNew[row] * k_col; //sK[out_dim];
new_state_val = fminf(fmaxf(new_state_val, -1e6f), 1e6f);
state_dst[row + out_dim * HEAD_DIM] = new_state_val;
}
}
}
}
}
// Generic kernel that handles any HEAD_DIM at runtime (slower but flexible)
__global__ void delta_net_recurrent_generic_f32(
const float * __restrict__ q,
const float * __restrict__ k,
const float * __restrict__ v,
const float * __restrict__ g,
const float * __restrict__ beta_in,
const float * __restrict__ state_in,
float * __restrict__ dst,
const int64_t head_dim,
const int64_t n_tokens,
const int64_t n_heads,
const int64_t n_seqs,
const int64_t output_offset,
const float eps) {
const int batch_idx = blockIdx.x / n_heads;
const int head_idx = blockIdx.x % n_heads;
const int tid = threadIdx.x;
// Strides (column-major)
const int64_t qkv_stride_token = head_dim;
const int64_t qkv_stride_head = head_dim * n_tokens;
const int64_t qkv_stride_batch = head_dim * n_tokens * n_heads;
const int64_t g_stride_head = n_tokens;
const int64_t g_stride_batch = n_tokens * n_heads;
const int64_t state_head_offset = head_idx * head_dim * head_dim;
const int64_t state_batch_stride = head_dim * head_dim * n_heads;
// Pointers
const float * q_ptr = q + batch_idx * qkv_stride_batch + head_idx * qkv_stride_head;
const float * k_ptr = k + batch_idx * qkv_stride_batch + head_idx * qkv_stride_head;
const float * v_ptr = v + batch_idx * qkv_stride_batch + head_idx * qkv_stride_head;
const float * g_ptr = g + batch_idx * g_stride_batch + head_idx * g_stride_head;
const float * beta_ptr = beta_in + batch_idx * g_stride_batch + head_idx * g_stride_head;
const float * state_src = state_in + batch_idx * state_batch_stride + state_head_offset;
// Output layout: [head_v_dim, num_v_heads, n_seq_tokens, n_seqs]
float * out_base = dst + batch_idx * (head_dim * n_heads * n_tokens) + head_idx * head_dim;
const int64_t out_token_stride = head_dim * n_heads;
float * state_dst = dst + output_offset + batch_idx * state_batch_stride + state_head_offset;
// Shared memory for scalars (outside loop)
__shared__ float shared_g_val, shared_beta_val, shared_decay, shared_attn_score;
// Dynamic shared memory
extern __shared__ float smem[];
float * sQ = smem;
float * sK = sQ + head_dim;
float * sV = sK + head_dim;
float * sKBeta = sV + head_dim; // plain k for state update
float * sVBeta = sKBeta + head_dim; // v * sigmoid(beta)
float * sOut = sVBeta + head_dim;
float * sKCumdecay = sOut + head_dim; // k * sigmoid(beta) * exp(g)
float * sVPrime = sKCumdecay + head_dim; // state @ k_cumdecay
float * sVNew = sVPrime + head_dim; // v_beta - v_prime
float * sNorm = sVNew + head_dim;
const float scale = rsqrtf((float)head_dim);
// Copy initial state to output buffer
for (int i = tid; i < head_dim * head_dim; i += blockDim.x) {
int col = i / head_dim;
int row = i % head_dim;
state_dst[row + col * head_dim] = state_src[row + col * head_dim];
}
__syncthreads();
// Process each token
for (int64_t t = 0; t < n_tokens; t++) {
if (tid < 2) sNorm[tid] = 0.0f;
__syncthreads();
// Load Q, K, V
for (int i = tid; i < head_dim; i += blockDim.x) {
sQ[i] = q_ptr[t * qkv_stride_token + i];
sK[i] = k_ptr[t * qkv_stride_token + i];
sV[i] = v_ptr[t * qkv_stride_token + i];
}
__syncthreads();
// L2 normalize Q and K
float q_sq = 0.0f, k_sq = 0.0f;
for (int i = tid; i < head_dim; i += blockDim.x) {
q_sq += sQ[i] * sQ[i];
k_sq += sK[i] * sK[i];
}
#pragma unroll
for (int offset = WARP_SIZE/2; offset > 0; offset /= 2) {
q_sq += __shfl_xor_sync(0xffffffff, q_sq, offset);
k_sq += __shfl_xor_sync(0xffffffff, k_sq, offset);
}
if (tid % WARP_SIZE == 0) {
atomicAdd(&sNorm[0], q_sq);
atomicAdd(&sNorm[1], k_sq);
}
__syncthreads();
float q_norm = rsqrtf(sNorm[0] + eps);
float k_norm = rsqrtf(sNorm[1] + eps);
for (int i = tid; i < head_dim; i += blockDim.x) {
sQ[i] *= q_norm * scale;
sK[i] *= k_norm;
}
__syncthreads();
// Load g and beta, compute decay
if (tid == 0) {
shared_g_val = g_ptr[t];
shared_beta_val = sigmoid_f(beta_ptr[t]);
shared_decay = expf(fminf(shared_g_val, 50.0f));
}
__syncthreads();
float beta_val = shared_beta_val;
float decay = shared_decay;
// Compute k_beta, v_beta, k_cumdecay
for (int i = tid; i < head_dim; i += blockDim.x) {
sKBeta[i] = sK[i];
sVBeta[i] = sV[i] * beta_val;
sKCumdecay[i] = sK[i] * beta_val * decay;
}
__syncthreads();
// Compute v_prime = state @ k_cumdecay
for (int row_out = tid; row_out < head_dim; row_out += blockDim.x) {
float v_prime_val = 0.0f;
for (int col = 0; col < head_dim; col++) {
// Access state[row_out, col] = state_dst[row_out + col * head_dim] for state @ k
v_prime_val += state_dst[row_out + col * head_dim] * sKCumdecay[col];
}
sVPrime[row_out] = v_prime_val;
}
__syncthreads();
// Compute v_new = v_beta - v_prime (the value residual)
for (int i = tid; i < head_dim; i += blockDim.x) {
sVNew[i] = sVBeta[i] - sVPrime[i];
}
__syncthreads();
// Compute attn_score = dot(k, q) (L2 normalized vectors)
if (tid == 0) {
float dot_sum = 0.0f;
for (int i = 0; i < head_dim; i++) {
dot_sum += sK[i] * sQ[i];
}
shared_attn_score = dot_sum;
}
__syncthreads();
// Compute output: o[t] = attn_inter + v_attn
// attn_inter = state @ (q * exp(g)) = sum_col(state[row_out, col] * q[col] * exp(g))
// The decomposed path uses: attn_inter = ggml_mul_mat(state_t, q_g_exp)
// Since ggml_mul_mat(A,B) = A^T @ B, attn_inter = state_t^T @ q_g_exp = state @ (q * exp(g))
for (int row_out = tid; row_out < head_dim; row_out += blockDim.x) {
float attn_inter = 0.0f;
for (int col = 0; col < head_dim; col++) {
// Access state[row_out, col] = state_dst[row_out + col * head_dim] for state @ q
float state_val = state_dst[row_out + col * head_dim];
attn_inter += sQ[col] * decay * state_val;
}
// v_attn = v_new * attn_score
float v_attn = sVNew[row_out] * shared_attn_score;
// Output = attn_inter + v_attn (correct DeltaNet formula)
sOut[row_out] = attn_inter + v_attn;
}
__syncthreads();
// Update state: state_new = decay * state + outer(v_new, k)
// Fixed: outer product orientation matches decomposed: state[v_idx, k_idx] += v_new[v_idx] * k[k_idx]
// Uses transposed indexing: state_dst[row + out_dim * head_dim] = state[row][out_dim]
// Only protect against NaN/Inf - do NOT clamp decay value
float safe_decay = decay;
if (isnan(safe_decay) || isinf(safe_decay)) {
safe_decay = 1.0f;
}
for (int out_dim = tid; out_dim < head_dim; out_dim += blockDim.x) {
for (int row = 0; row < head_dim; row++) {
float state_val = state_dst[row + out_dim * head_dim];
// state_new[row][out_dim] = decay * state[row][out_dim] + v_new[row] * k[out_dim]
// Fix: outer product matches decomposed path: state[v_idx, k_idx] += v_new[v_idx] * k[k_idx]
float new_state_val = safe_decay * state_val + sVNew[row] * sKBeta[out_dim];
// Clamp state to prevent overflow
new_state_val = fminf(fmaxf(new_state_val, -1e6f), 1e6f);
state_dst[row + out_dim * head_dim] = new_state_val;
}
}
__syncthreads();
// Write output
for (int i = tid; i < head_dim; i += blockDim.x) {
out_base[t * out_token_stride + i] = sOut[i];
}
__syncthreads();
}
}
static void delta_net_f32_cuda(
const float * q,
const float * k,
@@ -641,26 +178,6 @@ static void delta_net_f32_cuda(
const int64_t output_offset = head_dim * n_tokens * n_heads * n_seqs;
// One block per (batch, head) pair
//const int num_blocks = n_seqs * n_heads;
//constexpr int threads_per_block = 512; //256;
//const size_t smem_size = 4 * head_dim * sizeof(float);
//// Use templated kernel for common head dimensions, generic for others
//if (head_dim == 64) {
// delta_net_recurrent_f32<64, 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 if (head_dim == 128) {
// GGML_ASSERT(num_blocks % 8 == 0);
// 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);
//}
if (head_dim != 64 && head_dim != 128) {
GGML_ABORT("Unsupported delta net head size");
}
@@ -689,19 +206,6 @@ static void delta_net_f32_cuda(
}
}
//// Use templated kernel for common head dimensions, generic for others
//if (head_dim == 64) {
// delta_net_recurrent_f32_a<64, 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 if (head_dim == 128) {
// delta_net_recurrent_f32_a<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);
//}
CUDA_CHECK(cudaGetLastError());
}