@@ -341,7 +341,7 @@ ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer,
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assert (tensor->view_src ->buffer ->buft == buffer->buft );
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return GGML_STATUS_SUCCESS;
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}
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- if (tensor->type == GGML_TYPE_Q4_0 && !g_ggml_sycl_disable_optimize) {
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+ if (( tensor->type == GGML_TYPE_Q4_0 || tensor-> type == GGML_TYPE_Q4_K) && !g_ggml_sycl_disable_optimize) {
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ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{};
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tensor->extra = extra;
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ctx->tensor_extras .push_back (extra); // used to release it when destroy ctx.
@@ -2841,6 +2841,8 @@ inline bool ggml_sycl_supports_reorder_mul_mat_sycl(enum ggml_type type) {
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switch (type) {
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case GGML_TYPE_Q4_0:
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return true ;
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+ case GGML_TYPE_Q4_K:
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+ return !g_ggml_sycl_prioritize_dmmv;
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default :
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return false ;
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}
@@ -2858,6 +2860,7 @@ inline bool ggml_sycl_supports_reorder_dmmv(enum ggml_type type) {
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inline bool ggml_sycl_supports_reorder_mmvq (enum ggml_type type) {
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switch (type) {
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case GGML_TYPE_Q4_0:
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+ case GGML_TYPE_Q4_K:
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return true ;
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default :
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return false ;
@@ -2883,16 +2886,16 @@ static bool ggml_sycl_supports_dmmv(enum ggml_type type) {
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}
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}
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- static void reorder_qw ( char * data_device, const int ncols, const int nrows,
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- size_t size, size_t offset, dpct::queue_ptr stream) {
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- auto tmp_buf = sycl::malloc_shared<char >(size, *stream);
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+ static void reorder_qw_q4_0 ( uint8_t * data_device, const int ncols, const int nrows, size_t size, size_t offset ,
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+ dpct::queue_ptr stream) {
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+ auto * tmp_buf = sycl::malloc_shared<uint8_t >(size, *stream);
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SYCL_CHECK (
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CHECK_TRY_ERROR ((*stream).memcpy (tmp_buf, data_device, size)
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.wait ()));
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GGML_ASSERT ((size % sizeof (block_q4_0) == 0 ));
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GGML_ASSERT ((offset % sizeof (block_q4_0) == 0 ));
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int offset_blks = offset / sizeof (block_q4_0);
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- auto qs_ptr = ( uint8_t *) data_device + offset_blks * QK4_0 / 2 ;
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+ auto qs_ptr = data_device + offset_blks * QK4_0 / 2 ;
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auto d_ptr = (sycl::half*)(qs_ptr + ncols * nrows / 2 ) + offset_blks;
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stream->parallel_for (
@@ -2906,18 +2909,59 @@ static void reorder_qw(char *data_device, const int ncols, const int nrows,
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*(qs_ptr + ib * QK4_0 / 2 + j) = x[ib].qs [j];
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}
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*(d_ptr + ib) = x[ib].d ;
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- });
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+ }).wait_and_throw ();
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+
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+ sycl::free (tmp_buf, *stream);
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+ }
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+
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+ static void reorder_qw_q4_k (uint8_t * data_device, size_t size, size_t offset, dpct::queue_ptr stream) {
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+ GGML_ASSERT (size % sizeof (block_q4_K) == 0 );
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+ GGML_ASSERT (offset % sizeof (block_q4_K) == 0 );
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+
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+ const int nblocks = size / sizeof (block_q4_K);
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+
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+ auto * tmp_buf = sycl::malloc_shared<uint8_t >(size, *stream);
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+ SYCL_CHECK (CHECK_TRY_ERROR ((*stream).memcpy (tmp_buf, data_device, size).wait ()));
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+
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+ auto * qs_ptr = data_device;
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+ auto * scales_ptr = qs_ptr + QK_K / 2 * nblocks;
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+ auto * dm_ptr = (sycl::half2 *) (scales_ptr + K_SCALE_SIZE * nblocks);
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+
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+ stream->parallel_for (nblocks, [=](auto i) {
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+ const block_q4_K * x = (const block_q4_K *) tmp_buf;
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+ const int ib = i;
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+
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+ for (int j = 0 ; j < QK_K / 2 ; ++j) {
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+ qs_ptr[ib * (QK_K / 2 ) + j] = x[ib].qs [j];
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+ }
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+
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+ for (int j = 0 ; j < K_SCALE_SIZE; ++j) {
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+ scales_ptr[ib * K_SCALE_SIZE + j] = x[ib].scales [j];
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+ }
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+
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+ dm_ptr[ib] = x[ib].dm ;
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+ }).wait_and_throw ();
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sycl::free (tmp_buf, *stream);
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}
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static void reorder_qw (const ggml_tensor * src0, dpct::queue_ptr stream) {
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- char * data_device = (char *) src0->data ;
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+ uint8_t * data_device = (uint8_t *) src0->data ;
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size_t ncols = src0->ne [0 ];
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size_t nrows = src0->ne [1 ];
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size_t size = ggml_nbytes (src0);
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- reorder_qw (data_device, ncols, nrows, size, 0 , stream);
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+ switch (src0->type ) {
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+ case GGML_TYPE_Q4_0:
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+ reorder_qw_q4_0 (data_device, ncols, nrows, size, 0 , stream);
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+ break ;
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+ case GGML_TYPE_Q4_K:
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+ reorder_qw_q4_k (data_device, size, 0 , stream);
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+ break ;
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+ default :
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+ GGML_ABORT (" reorder_qw() called with unsupported type" );
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+ break ;
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+ }
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}
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static bool should_reorder_tensor (ggml_backend_sycl_context& ctx, const ggml_tensor * dst) {
@@ -2960,8 +3004,18 @@ static void opt_for_reorder(ggml_backend_sycl_context * ctx, const ggml_tensor *
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extra->optimized_feature .reorder = true ; // Used to decode/dequan in next steps and avoid re-reordering
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}
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- static void ggml_sycl_mul_mat (ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
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+ static bool can_use_dequantize_mul_mat_vec (const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
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+ return ggml_sycl_supports_dmmv (src0->type ) && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 &&
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+ src0->ne [0 ] % GGML_SYCL_DMMV_X == 0 && src1->ne [1 ] == 1 ;
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+ }
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+
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+ static bool can_use_mul_mat_vec_q (const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
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+ return ggml_is_quantized (src0->type ) && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 &&
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+ src1->ne [1 ] <= MMVQ_MAX_BATCH_SIZE;
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+ }
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+
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+ static void ggml_sycl_mul_mat (ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
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const bool split = ggml_backend_buffer_is_sycl_split (src0->buffer );
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int64_t min_compute_capability = INT_MAX;
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@@ -2984,13 +3038,9 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor
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}
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// check data types and tensor shapes for custom matrix multiplication kernels:
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- bool use_dequantize_mul_mat_vec = ggml_sycl_supports_dmmv (src0->type )
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- && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
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- && src0->ne [0 ] % GGML_SYCL_DMMV_X == 0 && src1->ne [1 ] == 1 ;
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+ bool use_dequantize_mul_mat_vec = can_use_dequantize_mul_mat_vec (src0, src1, dst);
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- bool use_mul_mat_vec_q = ggml_is_quantized (src0->type )
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- && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
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- && src1->ne [1 ] <= MMVQ_MAX_BATCH_SIZE;
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+ bool use_mul_mat_vec_q = can_use_mul_mat_vec_q (src0, src1, dst);
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bool use_mul_mat_q = ggml_sycl_supports_mmq (src0->type )
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&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
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