10x performance improve 4 cuda ssm conv & scan

ggml/src/ggml-cuda/ssm_conv.cu

@@ -7,10 +7,12 @@ static __global__ void ssm_conv_f32(
     const int src1_nb1,
     float * dst,
     const int dst_nb0, const int dst_nb1, const int dst_nb2,
-    const int nc, const int ncs, const int nr, const int n_t, const int n_s) {
+    const int nc, const int ncs, const int nr) {
 
 //    const int row = blockIdx.x*blockDim.y + threadIdx.y;
     const int tid = threadIdx.x;
+    const int i2 = blockIdx.x;
+    const int i3 = threadIdx.y;
 
     const int ith = tid;
     const int nth = WARP_SIZE;
@@ -23,30 +25,25 @@ static __global__ void ssm_conv_f32(
     const int ir1 = min(ir0 + dr, nr);
     const int ir  = ir1 - ir0;
     
-    for (int i3 = 0; i3 < n_s; ++i3) {
-        for (int i2 = 0; i2 < n_t; ++i2) {
     // {d_conv - 1 + n_t, d_inner, n_seqs}
     // sliding window
     const float * s = (const float *) ((const char *) src0 + ir0*src0_nb1 + i2*src0_nb0 + i3*src0_nb2); // {d_conv, d_inner, n_s}
     const float * c = (const float *) ((const char *) src1 + ir0*src1_nb1); // {d_conv, d_inner}
     float * x = (float *) ((char *) dst + ir0*dst_nb0 + i2*dst_nb1 + i3*dst_nb2); // {d_inner, n_t, n_s}
-
     // TODO: transpose the output for smaller strides for big batches?
     // d_inner
+    #pragma unroll
     for (int i1 = 0; i1 < ir; ++i1) {
         // rowwise dot product
         // NOTE: not using ggml_vec_dot_f32, because its sum is in double precision
         float sumf = 0.0f;
-
+        #pragma unroll
-                // d_conv
         for (int i0 = 0; i0 < nc; ++i0) {
             sumf += s[i0 + i1*ncs] * c[i0 + i1*nc];
         }
         x[i1] = sumf;
     }
 }
-    }
-}
 
 static void ssm_conv_f32_cuda(
     const float * src0, const float * src1,
@@ -57,8 +54,8 @@ static void ssm_conv_f32_cuda(
     const int nc, const int ncs, const int nr, const int n_t, const int n_s,
     cudaStream_t stream) {
 
-    const dim3 block_dims(WARP_SIZE, 1, 1);
+    const dim3 block_dims(WARP_SIZE, n_s, 1);
-    const int nblocks = 1; // TODO
+    const int nblocks = n_t; 
 
     ssm_conv_f32<WARP_SIZE><<<nblocks, block_dims, 0, stream>>>(
         src0, src1,
@@ -66,7 +63,7 @@ static void ssm_conv_f32_cuda(
         src1_nb1,
         dst,
         dst_nb0, dst_nb1, dst_nb2,
-        nc, ncs, nr, n_t, n_s);
+        nc, ncs, nr);
 }
 
 void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@@ -100,3 +97,4 @@ void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
         nc, ncs, nr, n_t, n_s,
         stream);
 }
+

ggml/src/ggml-cuda/ssm_scan.cu

@@ -11,10 +11,11 @@ static __global__ void ssm_scan_f32(
     const int src4_nb1, const int src4_nb2,
     const int src5_nb1, const int src5_nb2,
     float * dst,
-    const int nc, const int nr, const int n_t, const int n_s) {
+    const int nc, const int nr) {
 
-//    const int row = blockIdx.x*blockDim.y + threadIdx.y;
     const int tid = threadIdx.x;
+    const int i2 = blockIdx.x;
+    const int i3 = threadIdx.y;
 
     const int ith = tid;
     const int nth = WARP_SIZE;
@@ -27,8 +28,6 @@ static __global__ void ssm_scan_f32(
     const int ir1 = min(ir0 + dr, nr);
     const int ir  = ir1 - ir0;
 
-    for (int i3 = 0; i3 < n_s; ++i3) {
-        for (int i2 = 0; i2 < n_t; ++i2) {
     const float * s0 = (const float *) ((const char *) src0 + ir0*src0_nb1 + i3*src0_nb2); // {d_state, d_inner, n_s}
     const float * x  = (const float *) ((const char *) src1 + ir0*src1_nb0 + i2*src1_nb1 + i3*src1_nb2); // {d_inner, n_t, n_s}
     const float * dt = (const float *) ((const char *) src2 + ir0*src2_nb0 + i2*src2_nb1 + i3*src2_nb2); // {d_inner, n_t, n_s}
@@ -42,12 +41,14 @@ static __global__ void ssm_scan_f32(
     if (i2 > 0) { s0 = s; }
 
     // d_inner
+    #pragma unroll
     for (int i1 = 0; i1 < ir; ++i1) {
         // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78
         float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1];
         float x_dt = x[i1] * dt_soft_plus;
         float sumf = 0.0f;
         // d_state
+        #pragma unroll
         for (int i0 = 0; i0 < nc; ++i0) {
             int i = i0 + i1*nc;
             // state = prev_state * dA + dB * x
@@ -59,8 +60,6 @@ static __global__ void ssm_scan_f32(
         y[i1] = sumf;
     }
 }
-    }
-}
 
 static void ssm_scan_f32_cuda(
     const float * src0, const float * src1, const float * src2, const float * src3,
@@ -75,8 +74,8 @@ static void ssm_scan_f32_cuda(
     const int nc, const int nr, const int n_t, const int n_s,
     cudaStream_t stream) {
 
-    const dim3 block_dims(WARP_SIZE, 1, 1);
+    const dim3 block_dims(WARP_SIZE, n_s, 1);
-    const int nblocks = 1; // TODO
+    const int nblocks = n_t;
 
     ssm_scan_f32<WARP_SIZE><<<nblocks, block_dims, 0, stream>>>(
         src0, src1, src2, src3,
@@ -88,7 +87,7 @@ static void ssm_scan_f32_cuda(
         src4_nb1, src4_nb2,
         src5_nb1, src5_nb2,
         dst,
-        nc, nr, n_t, n_s);
+        nc, nr);
 }
 
 void ggml_cuda_op_ssm_scan(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {