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https://github.com/ggerganov/whisper.cpp.git
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ggml : add epsilon as a parameter for group_norm (llama/8818)
Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
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7a96e661e4
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@ -1140,16 +1140,17 @@ extern "C" {
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// group normalize along ne0*ne1*n_groups
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// used in stable-diffusion
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// TODO: eps is hardcoded to 1e-6 for now
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GGML_API struct ggml_tensor * ggml_group_norm(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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int n_groups);
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int n_groups,
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float eps);
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GGML_API struct ggml_tensor * ggml_group_norm_inplace(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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int n_groups);
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int n_groups,
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float eps);
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// a - x
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// b - dy
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@ -142,8 +142,7 @@ static void norm_f32_cuda(const float * x, float * dst, const int ncols, const i
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}
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}
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static void group_norm_f32_cuda(const float * x, float * dst, const int num_groups, const int group_size, const int ne_elements, cudaStream_t stream) {
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static const float eps = 1e-6f;
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static void group_norm_f32_cuda(const float * x, float * dst, const int num_groups, const float eps, const int group_size, const int ne_elements, cudaStream_t stream) {
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if (group_size < 1024) {
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const dim3 block_dims(WARP_SIZE, 1, 1);
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group_norm_f32<WARP_SIZE><<<num_groups, block_dims, 0, stream>>>(x, dst, group_size, ne_elements, eps);
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@ -196,8 +195,12 @@ void ggml_cuda_op_group_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
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GGML_ASSERT( dst->type == GGML_TYPE_F32);
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int num_groups = dst->op_params[0];
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float eps;
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memcpy(&eps, dst->op_params + 1, sizeof(float));
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int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);
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group_norm_f32_cuda(src0_d, dst_d, num_groups * src0->ne[3], group_size, ggml_nelements(src0), stream);
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group_norm_f32_cuda(src0_d, dst_d, num_groups * src0->ne[3], eps, group_size, ggml_nelements(src0), stream);
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}
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void ggml_cuda_op_rms_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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@ -2236,10 +2236,8 @@ static enum ggml_status ggml_metal_graph_compute(
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GGML_ASSERT(ne00 % 4 == 0);
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GGML_ASSERT(ggml_is_contiguous(src0));
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//float eps;
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//memcpy(&eps, dst->op_params, sizeof(float));
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const float eps = 1e-6f; // TODO: temporarily hardcoded
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float eps;
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memcpy(&eps, dst->op_params + 1, sizeof(float));
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const int32_t n_groups = ((int32_t *) dst->op_params)[0];
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@ -225,9 +225,8 @@ static void norm_f32_sycl(const float* x, float* dst, const int ncols,
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}
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static void group_norm_f32_sycl(const float* x, float* dst,
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const int num_groups, const int group_size,
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const int num_groups, const float eps, const int group_size,
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const int ne_elements, queue_ptr stream, int device) {
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static const float eps = 1e-6f;
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if (group_size < 1024) {
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const sycl::range<3> block_dims(1, 1, WARP_SIZE);
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stream->submit([&](sycl::handler& cgh) {
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@ -343,8 +342,12 @@ void ggml_sycl_op_group_norm(ggml_backend_sycl_context& ctx, const ggml_tensor*
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GGML_ASSERT(dst->type == GGML_TYPE_F32);
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int num_groups = dst->op_params[0];
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float eps;
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memcpy(&eps, dst->op_params + 1, sizeof(float));
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int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);
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group_norm_f32_sycl(src0_dd, dst_dd, num_groups, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream, ctx.device);
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group_norm_f32_sycl(src0_dd, dst_dd, num_groups, eps, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream, ctx.device);
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(void)src1;
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(void)dst;
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@ -5377,6 +5377,7 @@ static struct ggml_tensor * ggml_group_norm_impl(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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int n_groups,
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float eps,
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bool inplace) {
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bool is_node = false;
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@ -5387,7 +5388,8 @@ static struct ggml_tensor * ggml_group_norm_impl(
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struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
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result->op_params[0] = n_groups;
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ggml_set_op_params_i32(result, 0, n_groups);
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ggml_set_op_params_f32(result, 1, eps);
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result->op = GGML_OP_GROUP_NORM;
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result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
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@ -5399,15 +5401,17 @@ static struct ggml_tensor * ggml_group_norm_impl(
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struct ggml_tensor * ggml_group_norm(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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int n_groups) {
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return ggml_group_norm_impl(ctx, a, n_groups, false);
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int n_groups,
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float eps) {
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return ggml_group_norm_impl(ctx, a, n_groups, eps, false);
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}
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struct ggml_tensor * ggml_group_norm_inplace(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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int n_groups) {
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return ggml_group_norm_impl(ctx, a, n_groups, true);
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int n_groups,
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float eps) {
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return ggml_group_norm_impl(ctx, a, n_groups, eps, true);
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}
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// ggml_mul_mat
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@ -12098,10 +12102,11 @@ static void ggml_compute_forward_group_norm_f32(
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GGML_TENSOR_UNARY_OP_LOCALS
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const float eps = 1e-6f; // TODO: make this a parameter
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// TODO: optimize
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float eps;
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memcpy(&eps, dst->op_params + 1, sizeof(float));
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int n_channels = src0->ne[2];
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int n_groups = dst->op_params[0];
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int n_channels_per_group = (n_channels + n_groups - 1) / n_groups;
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