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Diffstat (limited to 'ggml/src/ggml-cuda/softmax.cu')
-rw-r--r--ggml/src/ggml-cuda/softmax.cu67
1 files changed, 52 insertions, 15 deletions
diff --git a/ggml/src/ggml-cuda/softmax.cu b/ggml/src/ggml-cuda/softmax.cu
index c24abae1..6f3056e6 100644
--- a/ggml/src/ggml-cuda/softmax.cu
+++ b/ggml/src/ggml-cuda/softmax.cu
@@ -12,7 +12,7 @@ __device__ float __forceinline__ t2f32<half>(half val) {
}
template <bool vals_smem, int ncols_template, int block_size_template, typename T>
-static __global__ void soft_max_f32(const float * x, const T * mask, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) {
+static __global__ void soft_max_f32(const float * x, const T * mask, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2, float cap_params0, float cap_params1, bool do_softcap) {
const int ncols = ncols_template == 0 ? ncols_par : ncols_template;
const int tid = threadIdx.x;
@@ -44,7 +44,8 @@ static __global__ void soft_max_f32(const float * x, const T * mask, float * dst
const int64_t ix = (int64_t)rowx*ncols + col;
const int64_t iy = (int64_t)rowy*ncols + col;
- const float val = x[ix]*scale + (mask ? slope*t2f32(mask[iy]) : 0.0f);
+ const float val = do_softcap ? scale*cap_params1*tanhf(cap_params0*x[ix]) + (mask ? slope*t2f32(mask[iy]) : 0.0f) :
+ scale*x[ix] + (mask ? slope*t2f32(mask[iy]) : 0.0f);
vals[col] = val;
max_val = max(max_val, val);
@@ -116,7 +117,7 @@ static __global__ void soft_max_f32(const float * x, const T * mask, float * dst
}
template<typename T>
-static void soft_max_f32_cuda(const float * x, const T * mask, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) {
+static void soft_max_f32_cuda(const float * x, const T * mask, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, float cap_params0, float cap_params1, bool do_softcap, cudaStream_t stream) {
int nth = WARP_SIZE;
while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2;
const dim3 block_dims(nth, 1, 1);
@@ -134,36 +135,36 @@ static void soft_max_f32_cuda(const float * x, const T * mask, float * dst, cons
if (shmem < ggml_cuda_info().devices[ggml_cuda_get_device()].smpb) {
switch (ncols_x) {
case 32:
- soft_max_f32<true, 32, 32><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+ soft_max_f32<true, 32, 32><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, cap_params0, cap_params1, do_softcap);
break;
case 64:
- soft_max_f32<true, 64, 64><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+ soft_max_f32<true, 64, 64><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, cap_params0, cap_params1, do_softcap);
break;
case 128:
- soft_max_f32<true, 128, 128><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+ soft_max_f32<true, 128, 128><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, cap_params0, cap_params1, do_softcap);
break;
case 256:
- soft_max_f32<true, 256, 256><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+ soft_max_f32<true, 256, 256><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, cap_params0, cap_params1, do_softcap);
break;
case 512:
- soft_max_f32<true, 512, 512><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+ soft_max_f32<true, 512, 512><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, cap_params0, cap_params1, do_softcap);
break;
case 1024:
- soft_max_f32<true, 1024, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+ soft_max_f32<true, 1024, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, cap_params0, cap_params1, do_softcap);
break;
case 2048:
- soft_max_f32<true, 2048, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+ soft_max_f32<true, 2048, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, cap_params0, cap_params1, do_softcap);
break;
case 4096:
- soft_max_f32<true, 4096, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+ soft_max_f32<true, 4096, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, cap_params0, cap_params1, do_softcap);
break;
default:
- soft_max_f32<true, 0, 0><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+ soft_max_f32<true, 0, 0><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, cap_params0, cap_params1, do_softcap);
break;
}
} else {
const size_t shmem_low = WARP_SIZE*sizeof(float);
- soft_max_f32<false, 0, 0><<<block_nums, block_dims, shmem_low, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
+ soft_max_f32<false, 0, 0><<<block_nums, block_dims, shmem_low, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, cap_params0, cap_params1, do_softcap);
}
}
@@ -197,10 +198,46 @@ void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
if (use_f16) {
const half * src1_dd = (const half *)src1_d;
- soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
+ soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, 0, 0, false, stream);
} else {
const float * src1_dd = (const float *)src1_d;
- soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
+ soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, 0, 0, false, stream);
+ }
+}
+
+void ggml_cuda_op_soft_cap_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+ const ggml_tensor * src0 = dst->src[0];
+ const ggml_tensor * src1 = dst->src[1];
+
+ const float * src0_d = (const float *)src0->data;
+ const void * src1_d = src1 ? (const void *)src1->data : nullptr;
+
+ float * dst_d = (float *)dst->data;
+ cudaStream_t stream = ctx.stream();
+
+ GGML_ASSERT(src0->type == GGML_TYPE_F32);
+ GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+ GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
+
+ const int64_t ne00 = src0->ne[0];
+ const int64_t nrows_x = ggml_nrows(src0);
+ const int64_t nrows_y = src0->ne[1];
+
+ float params[4];
+ memcpy(params, dst->op_params, sizeof(params));
+
+ const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16);
+ //printf("%s: %g, %g, %g, %g, %p, %d\n", __func__, params[0], params[1], params[2], params[3], (const void *)src1, use_f16);
+
+ if (use_f16) {
+ const half * src1_dd = (const half *)src1_d;
+
+ soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, params[0], params[1], params[2], params[3], true, stream);
+ } else {
+ const float * src1_dd = (const float *)src1_d;
+
+ soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, params[0], params[1], params[2], params[3], true, stream);
}
}
generated by cgit v1.2.3 (git 2.25.1) at 2025-08-21 17:47:09 +0000