use template function directly
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hongruichen
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1 changed files with 26 additions and 159 deletions
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@ -269,168 +269,35 @@ void qnn_binary_op_impl(ggml_backend_qnn_context *ctx, const ggml_tensor *src0,
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} // namespace
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static void ggml_qnn_add(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {
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qnn_binary_op_impl<GGML_OP_ADD>(ctx, src0, src1, dst);
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}
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static void ggml_qnn_mul(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {
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qnn_binary_op_impl<GGML_OP_MUL>(ctx, src0, src1, dst);
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}
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/*
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* ggml_qnn_mul_mat was re-added as a standalone function because
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* the following comments came from https://github.com/ggerganov/llama.cpp/pull/1632
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* MUL_MAT take most of the compute time (about 95%).
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* So to speed up llama, we have to focus on MUL_MAT.
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*
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* We have three kinds of MUL_MAT to compute:
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* mul_mat_f32: both src0 and src1 are F32.
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* mul_mat_f16_f32: src0 is F16 and src1 is F32.
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* mul_mat_q_f32: src0 is quantized (Q4_0, Q4_1, ...), and src1 is F32.
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*/
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static void ggml_qnn_mul_mat(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {
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qnn_binary_op_impl<GGML_OP_MUL_MAT>(ctx, src0, src1, dst);
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}
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static void ggml_qnn_repeat(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_get_rows(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_acc(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_div(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_gelu(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_silu(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_gelu_quick(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_tanh(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_relu(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_hardsigmoid(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_hardswish(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_leaky_relu(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_sqr(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_sqrt(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {
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qnn_binary_op_impl<GGML_OP_SQRT>(ctx, src0, src1, dst);
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}
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static void ggml_qnn_norm(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_group_norm(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_concat(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_upscale(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_pad(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_rms_norm(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_cpy(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_dup(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {
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ggml_qnn_cpy(ctx, src0, dst, nullptr);
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(void)src1;
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}
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static void ggml_qnn_mul_mat_id(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_scale(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_clamp(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_diag_mask_inf(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_soft_max(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_rope(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {
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GGML_ASSERT(ggml_is_contiguous(src0));
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}
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static void ggml_qnn_pool2d(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_im2col(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {}
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static void ggml_qnn_sum_rows(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {
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GGML_ASSERT(ggml_is_contiguous(src0));
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}
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static void ggml_qnn_argsort(ggml_backend_qnn_context *ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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ggml_tensor *dst) {
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GGML_ASSERT(ggml_is_contiguous(src0));
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}
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qnn::ggml_qnn_op_array_t qnn::ggml_qnn_op_array() {
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static constexpr const qnn::ggml_qnn_op_t kQnnOpsTable[] = {
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nullptr, // GGML_OP_NONE
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nullptr, // GGML_OP_DUP
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ggml_qnn_add, // GGML_OP_ADD
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nullptr, // GGML_OP_ADD1
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nullptr, // GGML_OP_ACC
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nullptr, // GGML_OP_SUB
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ggml_qnn_mul, // GGML_OP_MUL
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nullptr, // GGML_OP_DIV
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nullptr, // GGML_OP_SQR
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ggml_qnn_sqrt, // GGML_OP_SQRT
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nullptr, // GGML_OP_LOG
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nullptr, // GGML_OP_SUM
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nullptr, // GGML_OP_SUM_ROWS
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nullptr, // GGML_OP_MEAN
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nullptr, // GGML_OP_ARGMAX
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nullptr, // GGML_OP_REPEAT
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nullptr, // GGML_OP_REPEAT_BACK
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nullptr, // GGML_OP_CONCAT
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nullptr, // GGML_OP_SILU_BACK
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nullptr, // GGML_OP_NORM
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nullptr, // GGML_OP_RMS_NORM
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nullptr, // GGML_OP_RMS_NORM_BACK
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nullptr, // GGML_OP_GROUP_NORM
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nullptr, // GGML_OP_NONE
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nullptr, // GGML_OP_DUP
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qnn_binary_op_impl<GGML_OP_ADD>, // GGML_OP_ADD
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nullptr, // GGML_OP_ADD1
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nullptr, // GGML_OP_ACC
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nullptr, // GGML_OP_SUB
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qnn_binary_op_impl<GGML_OP_MUL>, // GGML_OP_MUL
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nullptr, // GGML_OP_DIV
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nullptr, // GGML_OP_SQR
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qnn_binary_op_impl<GGML_OP_SQRT>, // GGML_OP_SQRT
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nullptr, // GGML_OP_LOG
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nullptr, // GGML_OP_SUM
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nullptr, // GGML_OP_SUM_ROWS
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nullptr, // GGML_OP_MEAN
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nullptr, // GGML_OP_ARGMAX
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nullptr, // GGML_OP_REPEAT
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nullptr, // GGML_OP_REPEAT_BACK
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nullptr, // GGML_OP_CONCAT
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nullptr, // GGML_OP_SILU_BACK
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nullptr, // GGML_OP_NORM
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nullptr, // GGML_OP_RMS_NORM
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nullptr, // GGML_OP_RMS_NORM_BACK
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nullptr, // GGML_OP_GROUP_NORM
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ggml_qnn_mul_mat, // GGML_OP_MUL_MAT
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nullptr, // GGML_OP_MUL_MAT_ID
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nullptr, // GGML_OP_OUT_PROD
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qnn_binary_op_impl<GGML_OP_MUL_MAT>, // GGML_OP_MUL_MAT
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nullptr, // GGML_OP_MUL_MAT_ID
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nullptr, // GGML_OP_OUT_PROD
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nullptr, // GGML_OP_SCALE
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nullptr, // GGML_OP_SET
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