mirror of
https://github.com/ggerganov/llama.cpp.git
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CUDA: revise q8_1 data layout for mul_mat_q (#7824)
This commit is contained in:
Johannes Gäßler
GitHub
parent
2decf57bc6
commit
42b53d192f
86
ggml-cuda.cu
86
ggml-cuda.cu
@ -1347,10 +1347,30 @@ static void ggml_cuda_set_peer_access(const int n_tokens, int main_device) {
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GGML_UNUSED(main_device);
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}
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static cudaError_t ggml_cuda_Memcpy2DPeerAsync(
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void * dst, int dstDevice, size_t dpitch, void * src, int srcDevice, size_t spitch, size_t width, size_t height, cudaStream_t stream) {
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#if !defined(GGML_USE_HIPBLAS)
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// cudaMemcpy2DAsync may fail with copies between vmm pools of different devices
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cudaMemcpy3DPeerParms p = {};
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p.dstDevice = dstDevice;
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p.dstPtr = make_cudaPitchedPtr(dst, dpitch, dpitch, height);
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p.srcDevice = srcDevice;
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p.srcPtr = make_cudaPitchedPtr(src, spitch, spitch, height);
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p.extent = make_cudaExtent(width, height, 1);
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return cudaMemcpy3DPeerAsync(&p, stream);
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#else
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// HIP does not support cudaMemcpy3DPeerAsync or vmm pools
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GGML_UNUSED(dstDevice);
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GGML_UNUSED(srcDevice);
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return cudaMemcpy2DAsync(dst, dpitch, src, spitch, width, height, cudaMemcpyDeviceToDevice, stream);
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#endif // !defined(GGML_USE_HIPBLAS)
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}
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static void ggml_cuda_op_mul_mat(
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ggml_backend_cuda_context & ctx,
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const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_cuda_op_mul_mat_t op,
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const bool convert_src1_to_q8_1) {
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quantize_cuda_t quantize_src1) {
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const int64_t ne00 = src0->ne[0];
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const int64_t ne01 = src0->ne[1];
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@ -1407,6 +1427,8 @@ static void ggml_cuda_op_mul_mat(
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}
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struct dev_data {
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int cc;
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ggml_cuda_pool_alloc<char> src0_dd_alloc;
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ggml_cuda_pool_alloc<float> src1_ddf_alloc;
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ggml_cuda_pool_alloc<char> src1_ddq_alloc;
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@ -1426,6 +1448,8 @@ static void ggml_cuda_op_mul_mat(
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int used_devices = 0;
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for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
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dev[id].cc = ggml_cuda_info().devices[id].cc;
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// by default, use all rows
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dev[id].row_low = 0;
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dev[id].row_high = ne01;
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@ -1476,11 +1500,15 @@ static void ggml_cuda_op_mul_mat(
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dev[id].src1_ddf = dev[id].src1_ddf_alloc.alloc(ctx.pool(id), ggml_nelements(src1));
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}
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if (convert_src1_to_q8_1) {
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dev[id].src1_ddq = dev[id].src1_ddq_alloc.alloc(ctx.pool(id), nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs);
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if (quantize_src1) {
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size_t src_1_ddq_size = nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs;
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if (quantize_src1 == quantize_mmq_q8_1_cuda) {
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src_1_ddq_size += get_mmq_x_max_host(dev[id].cc)*sizeof(block_q8_1_mmq);
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}
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dev[id].src1_ddq = dev[id].src1_ddq_alloc.alloc(ctx.pool(id), src_1_ddq_size);
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if (src1_on_device && src1_is_contiguous) {
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quantize_row_q8_1_cuda(dev[id].src1_ddf, dev[id].src1_ddq, ne10, nrows1, src1_padded_col_size, stream);
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quantize_src1(dev[id].src1_ddf, dev[id].src1_ddq, ne10, ne11, ne12*ne13, src1_padded_col_size, src0->type, stream);
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CUDA_CHECK(cudaGetLastError());
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}
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}
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@ -1526,7 +1554,12 @@ static void ggml_cuda_op_mul_mat(
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const int64_t i03 = i0 / ne12;
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const int64_t i02 = i0 % ne12;
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const size_t src1_ddq_i_offset = (i0*ne11 + src1_col_0) * src1_padded_col_size*q8_1_ts/q8_1_bs;
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size_t src1_ddq_i_offset = i0*ne11 * src1_padded_col_size*q8_1_ts/q8_1_bs;
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if (quantize_src1 == quantize_mmq_q8_1_cuda) {
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src1_ddq_i_offset += src1_col_0 * sizeof(block_q8_1_mmq);
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} else {
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src1_ddq_i_offset += src1_col_0 * src1_padded_col_size*q8_1_ts/q8_1_bs;
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}
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// for split tensors the data begins at i0 == i0_offset_low
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char * src0_dd_i = dev[id].src0_dd + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs;
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@ -1543,10 +1576,17 @@ static void ggml_cuda_op_mul_mat(
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// copy src0, src1 to device if necessary
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if (src1_is_contiguous) {
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if (id != ctx.device) {
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if (convert_src1_to_q8_1) {
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if (quantize_src1) {
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char * src1_ddq_i_source = dev[ctx.device].src1_ddq + src1_ddq_i_offset;
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CUDA_CHECK(cudaMemcpyPeerAsync(src1_ddq_i, id, src1_ddq_i_source, ctx.device,
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src1_ncols*src1_padded_col_size*q8_1_ts/q8_1_bs, stream));
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if (quantize_src1 == quantize_mmq_q8_1_cuda) {
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const size_t pitch = ne11*sizeof(block_q8_1_mmq);
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const size_t width = src1_ncols*sizeof(block_q8_1_mmq);
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const size_t height = src1_padded_col_size/(4*QK8_1);
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CUDA_CHECK(ggml_cuda_Memcpy2DPeerAsync(src1_ddq_i, id, pitch, src1_ddq_i_source, ctx.device, pitch, width, height, stream));
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} else {
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CUDA_CHECK(cudaMemcpyPeerAsync(
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src1_ddq_i, id, src1_ddq_i_source, ctx.device, src1_ncols*src1_padded_col_size*q8_1_ts/q8_1_bs, stream));
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}
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} else {
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float * src1_ddf_i_source = (float *) src1->data;
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src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10;
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@ -1561,8 +1601,8 @@ static void ggml_cuda_op_mul_mat(
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GGML_ASSERT(false);
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}
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if (convert_src1_to_q8_1 && !src1_is_contiguous) {
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quantize_row_q8_1_cuda(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream);
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if (quantize_src1 && !src1_is_contiguous) {
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quantize_src1(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, 1, src1_padded_col_size, src0->type, stream);
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CUDA_CHECK(cudaGetLastError());
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}
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@ -1587,22 +1627,8 @@ static void ggml_cuda_op_mul_mat(
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float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
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GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
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dhf_dst_i += src1_col_0*ne0 + dev[id].row_low;
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#if !defined(GGML_USE_HIPBLAS)
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// cudaMemcpy2DAsync may fail with copies between vmm pools of different devices
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cudaMemcpy3DPeerParms p = {};
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p.dstDevice = ctx.device;
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p.dstPtr = make_cudaPitchedPtr(dhf_dst_i, ne0*sizeof(float), row_diff, src1_ncols);
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p.srcDevice = id;
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p.srcPtr = make_cudaPitchedPtr(dst_dd_i, row_diff*sizeof(float), row_diff, src1_ncols);
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p.extent = make_cudaExtent(row_diff*sizeof(float), src1_ncols, 1);
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CUDA_CHECK(cudaMemcpy3DPeerAsync(&p, stream));
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#else
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// HIP does not support cudaMemcpy3DPeerAsync or vmm pools
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CUDA_CHECK(cudaMemcpy2DAsync(dhf_dst_i, ne0*sizeof(float),
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dst_dd_i, row_diff*sizeof(float),
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row_diff*sizeof(float), src1_ncols,
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cudaMemcpyDeviceToDevice, stream));
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#endif
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CUDA_CHECK(ggml_cuda_Memcpy2DPeerAsync(
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dhf_dst_i, ctx.device, ne0*sizeof(float), dst_dd_i, id, row_diff*sizeof(float), row_diff*sizeof(float), src1_ncols, stream));
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} else {
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float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
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GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
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@ -1941,13 +1967,13 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
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// KQ + KQV multi-batch
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ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst);
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} else if (use_dequantize_mul_mat_vec) {
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ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false);
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ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, nullptr);
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} else if (use_mul_mat_vec_q) {
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ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true);
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ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, quantize_row_q8_1_cuda);
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} else if (use_mul_mat_q) {
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ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_q, true);
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ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_q, quantize_mmq_q8_1_cuda);
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} else {
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ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);
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ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_cublas, nullptr);
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}
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}
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@ -11,6 +11,7 @@ void ggml_cuda_op_mul_mat_q(
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const int64_t nb01 = src0->nb[1];
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const int64_t ne10 = src1->ne[0];
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const int64_t ne11 = src1->ne[1];
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GGML_ASSERT(ne10 % QK8_1 == 0);
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const int64_t ne0 = dst->ne[0];
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@ -25,7 +26,7 @@ void ggml_cuda_op_mul_mat_q(
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// nrows_dst == nrows of the matrix that the kernel writes into
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const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff;
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const mmq_args args = {src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stride00, src1_padded_row_size, src1_ncols, nrows_dst};
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const mmq_args args = {src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stride00, src1_padded_row_size, src1_ncols, ne11, nrows_dst};
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switch (src0->type) {
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case GGML_TYPE_Q4_0:
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@ -1,15 +1,26 @@
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#pragma once
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#include "common.cuh"
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#include "vecdotq.cuh"
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#include <climits>
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#include <cstdint>
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#define MMQ_TILE_Y_K (WARP_SIZE + WARP_SIZE/QI8_1)
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typedef void (*load_tiles_mmq_t)(
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const char * __restrict__ x, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
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int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride);
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typedef void (*vec_dot_mmq_t)(
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const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
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const int * __restrict__ y_qs, const half2 * __restrict__ y_ms, float * __restrict__ sum, const int & k0);
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const int * __restrict__ y, float * __restrict__ sum, const int & k0);
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struct block_q8_1_mmq {
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half2 ds[4];
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int8_t qs[4*QK8_1];
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};
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static_assert(sizeof(block_q8_1_mmq) == 4*QK8_1 + 4*sizeof(half2), "Unexpected block_q8_1_mmq size");
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static_assert(sizeof(block_q8_1_mmq) == 4*sizeof(block_q8_1), "Unexpected block_q8_1_mmq size");
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struct tile_x_sizes {
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int ql;
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@ -132,10 +143,14 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
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template <int mmq_x, int mmq_y, int nwarps>
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static __device__ __forceinline__ void vec_dot_q4_0_q8_1_mul_mat(
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const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
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const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, float * __restrict__ sum, const int & k0) {
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const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
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GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
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const float * x_dmf = (const float *) x_dm;
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const int * y_qs = (const int *) y + 4;
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const half2 * y_ds = (const half2 *) y;
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#pragma unroll
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for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
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const int j = j0 + threadIdx.y;
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@ -145,19 +160,18 @@ static __device__ __forceinline__ void vec_dot_q4_0_q8_1_mul_mat(
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const int i = i0 + threadIdx.x;
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const int kyqs = k0 % (QI8_1/2) + QI8_1 * (k0 / (QI8_1/2));
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const float * x_dmf = (const float *) x_dm;
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int u[2*VDR_Q4_0_Q8_1_MMQ];
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#pragma unroll
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for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
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u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE];
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u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_0) % WARP_SIZE];
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u[2*l+0] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l) % WARP_SIZE];
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u[2*l+1] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l + QI4_0) % WARP_SIZE];
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}
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sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
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(&x_ql[i*(WARP_SIZE + 1) + k0], u, x_dmf[i*(WARP_SIZE/QI4_0) + i/QI4_0 + k0/QI4_0],
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y_ds[j * (WARP_SIZE/QI8_1) + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]);
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y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]);
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}
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}
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}
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@ -203,10 +217,13 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
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template <int mmq_x, int mmq_y, int nwarps>
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static __device__ __forceinline__ void vec_dot_q4_1_q8_1_mul_mat(
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const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
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const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, float * __restrict__ sum, const int & k0) {
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const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
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GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
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const int * y_qs = (const int *) y + 4;
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const half2 * y_ds = (const half2 *) y;
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#pragma unroll
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for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
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const int j = j0 + threadIdx.y;
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@ -221,13 +238,13 @@ static __device__ __forceinline__ void vec_dot_q4_1_q8_1_mul_mat(
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#pragma unroll
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for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
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u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE];
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u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_1) % WARP_SIZE];
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u[2*l+0] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l) % WARP_SIZE];
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u[2*l+1] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l + QI4_1) % WARP_SIZE];
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}
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sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
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(&x_ql[i*(WARP_SIZE + 1) + k0], u, x_dm[i*(WARP_SIZE/QI4_1) + i/QI4_1 + k0/QI4_1],
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y_ds[j * (WARP_SIZE/QI8_1) + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]);
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y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]);
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}
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}
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}
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@ -293,10 +310,14 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
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template <int mmq_x, int mmq_y, int nwarps>
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static __device__ __forceinline__ void vec_dot_q5_0_q8_1_mul_mat(
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const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
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const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, float * __restrict__ sum, const int & k0) {
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const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
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GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
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const float * x_dmf = (const float *) x_dm;
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const int * y_qs = (const int *) y + 4;
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const float * y_df = (const float *) y;
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#pragma unroll
|
||||
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
@ -307,19 +328,17 @@ static __device__ __forceinline__ void vec_dot_q5_0_q8_1_mul_mat(
|
||||
|
||||
const int kyqs = k0 % (QI8_1/2) + QI8_1 * (k0 / (QI8_1/2));
|
||||
const int index_bx = i*(WARP_SIZE/QI5_0) + i/QI5_0 + k0/QI5_0;
|
||||
const float * x_dmf = (const float *) x_dm;
|
||||
const float * y_df = (const float *) y_ds;
|
||||
|
||||
int u[2*VDR_Q5_0_Q8_1_MMQ];
|
||||
|
||||
#pragma unroll
|
||||
for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) {
|
||||
u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE];
|
||||
u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE];
|
||||
u[2*l+0] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l) % WARP_SIZE];
|
||||
u[2*l+1] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l + QI5_0) % WARP_SIZE];
|
||||
}
|
||||
|
||||
sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q8_0_q8_1_impl<float, QR5_0*VDR_Q5_0_Q8_1_MMQ>
|
||||
(&x_ql[i * (2*WARP_SIZE + 1) + 2 * k0], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]);
|
||||
(&x_ql[i*(2*WARP_SIZE + 1) + 2*k0], u, x_dmf[index_bx], y_df[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]);
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -383,10 +402,13 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
|
||||
template <int mmq_x, int mmq_y, int nwarps>
|
||||
static __device__ __forceinline__ void vec_dot_q5_1_q8_1_mul_mat(
|
||||
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
||||
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, float * __restrict__ sum, const int & k0) {
|
||||
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
|
||||
|
||||
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
|
||||
|
||||
const int * y_qs = (const int *) y + 4;
|
||||
const half2 * y_ds = (const half2 *) y;
|
||||
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
@ -396,18 +418,18 @@ static __device__ __forceinline__ void vec_dot_q5_1_q8_1_mul_mat(
|
||||
const int i = i0 + threadIdx.x;
|
||||
|
||||
const int kyqs = k0 % (QI8_1/2) + QI8_1 * (k0 / (QI8_1/2));
|
||||
const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k0/QI5_1;
|
||||
const int index_bx = i*(WARP_SIZE/QI5_1) + i/QI5_1 + k0/QI5_1;
|
||||
|
||||
int u[2*VDR_Q5_1_Q8_1_MMQ];
|
||||
|
||||
#pragma unroll
|
||||
for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) {
|
||||
u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE];
|
||||
u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_1) % WARP_SIZE];
|
||||
u[2*l+0] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l) % WARP_SIZE];
|
||||
u[2*l+1] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l + QI5_1) % WARP_SIZE];
|
||||
}
|
||||
|
||||
sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
|
||||
(&x_ql[i * (2*WARP_SIZE + 1) + 2 * k0], u, x_dm[index_bx], y_ds[j * (WARP_SIZE/QI8_1) + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]);
|
||||
(&x_ql[i*(2*WARP_SIZE + 1) + 2*k0], u, x_dm[index_bx], y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]);
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -455,10 +477,14 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
|
||||
template <int mmq_x, int mmq_y, int nwarps>
|
||||
static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mul_mat(
|
||||
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
||||
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, float * __restrict__ sum, const int & k0) {
|
||||
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
|
||||
|
||||
GGML_UNUSED(x_qh); GGML_UNUSED(x_sc);
|
||||
|
||||
const float * x_dmf = (const float *) x_dm;
|
||||
const int * y_qs = (const int *) y + 4;
|
||||
const float * y_df = (const float *) y;
|
||||
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
@ -467,12 +493,9 @@ static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mul_mat(
|
||||
for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
|
||||
const float * x_dmf = (const float *) x_dm;
|
||||
const float * y_df = (const float *) y_ds;
|
||||
|
||||
sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q8_0_q8_1_impl<float, VDR_Q8_0_Q8_1_MMQ>
|
||||
(&x_ql[i * (WARP_SIZE + 1) + k0], &y_qs[j * WARP_SIZE + k0], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k0/QI8_0],
|
||||
y_df[j * (WARP_SIZE/QI8_1) + k0/QI8_1]);
|
||||
(&x_ql[i*(WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k0], x_dmf[i*(WARP_SIZE/QI8_0) + i/QI8_0 + k0/QI8_0],
|
||||
y_df[j*MMQ_TILE_Y_K + k0/QI8_1]);
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -531,10 +554,13 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
|
||||
template <int mmq_x, int mmq_y, int nwarps>
|
||||
static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mul_mat(
|
||||
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
||||
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, float * __restrict__ sum, const int & k0) {
|
||||
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
|
||||
|
||||
GGML_UNUSED(x_qh);
|
||||
|
||||
const int * y_qs = (const int *) y + 4;
|
||||
const float * y_df = (const float *) y;
|
||||
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
@ -545,7 +571,6 @@ static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mul_mat(
|
||||
|
||||
const int kbx = k0 / QI2_K;
|
||||
const int ky = (k0 % QI2_K) * QR2_K;
|
||||
const float * y_df = (const float *) y_ds;
|
||||
|
||||
int v[QR2_K*VDR_Q2_K_Q8_1_MMQ];
|
||||
|
||||
@ -559,9 +584,9 @@ static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mul_mat(
|
||||
|
||||
const uint8_t * scales = ((const uint8_t *) &x_sc[i*(WARP_SIZE/4) + i/4 + kbx*4]) + ky/4;
|
||||
|
||||
const int index_y = j * WARP_SIZE + (QR2_K*k0) % WARP_SIZE;
|
||||
sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q2_K_q8_1_impl_mmq(
|
||||
v, &y_qs[index_y], scales, x_dm[i * (WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[index_y/QI8_1]);
|
||||
v, &y_qs[j*MMQ_TILE_Y_K + (QR2_K*k0) % WARP_SIZE], scales,
|
||||
x_dm[i*(WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[j*MMQ_TILE_Y_K + ((QR2_K*k0) % WARP_SIZE)/QI8_1]);
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -646,7 +671,11 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
|
||||
template <int mmq_x, int mmq_y, int nwarps>
|
||||
static __device__ __forceinline__ void vec_dot_q3_K_q8_1_mul_mat(
|
||||
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
||||
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, float * __restrict__ sum, const int & k0) {
|
||||
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
|
||||
|
||||
const float * x_dmf = (const float *) x_dm;
|
||||
const int * y_qs = (const int *) y + 4;
|
||||
const float * y_df = (const float *) y;
|
||||
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
|
||||
@ -658,8 +687,6 @@ static __device__ __forceinline__ void vec_dot_q3_K_q8_1_mul_mat(
|
||||
|
||||
const int kbx = k0 / QI3_K;
|
||||
const int ky = (k0 % QI3_K) * QR3_K;
|
||||
const float * x_dmf = (const float *) x_dm;
|
||||
const float * y_df = (const float *) y_ds;
|
||||
|
||||
const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4;
|
||||
|
||||
@ -677,9 +704,9 @@ static __device__ __forceinline__ void vec_dot_q3_K_q8_1_mul_mat(
|
||||
v[l] = __vsubss4(vll, vlh);
|
||||
}
|
||||
|
||||
const int index_y = j * WARP_SIZE + (k0*QR3_K) % WARP_SIZE;
|
||||
sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q3_K_q8_1_impl_mmq(
|
||||
v, &y_qs[index_y], scales, x_dmf[i * (WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[index_y/QI8_1]);
|
||||
v, &y_qs[j*MMQ_TILE_Y_K + (k0*QR3_K) % WARP_SIZE], scales,
|
||||
x_dmf[i*(WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[j*MMQ_TILE_Y_K + ((k0*QR3_K) % WARP_SIZE)/QI8_1]);
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -746,10 +773,13 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
|
||||
template <int mmq_x, int mmq_y, int nwarps>
|
||||
static __device__ __forceinline__ void vec_dot_q4_K_q8_1_mul_mat(
|
||||
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
||||
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, float * __restrict__ sum, const int & k0) {
|
||||
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
|
||||
|
||||
GGML_UNUSED(x_qh);
|
||||
|
||||
const int * y_qs = (const int *) y + 4;
|
||||
const half2 * y_ds = (const half2 *) y;
|
||||
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
@ -760,9 +790,9 @@ static __device__ __forceinline__ void vec_dot_q4_K_q8_1_mul_mat(
|
||||
|
||||
const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/16]) + 2*((k0 % 16) / 8);
|
||||
|
||||
const int index_y = j * WARP_SIZE + (QR4_K*k0) % WARP_SIZE;
|
||||
sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_K_q8_1_impl_mmq(
|
||||
&x_ql[i * (WARP_SIZE + 1) + k0], &y_qs[index_y], sc, sc+8, x_dm[i * (WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[index_y/QI8_1]);
|
||||
&x_ql[i*(WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + (QR4_K*k0) % WARP_SIZE], sc, sc+8,
|
||||
x_dm[i*(WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[j*MMQ_TILE_Y_K + ((QR4_K*k0) % WARP_SIZE)/QI8_1]);
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -842,10 +872,13 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
|
||||
template <int mmq_x, int mmq_y, int nwarps>
|
||||
static __device__ __forceinline__ void vec_dot_q5_K_q8_1_mul_mat(
|
||||
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
||||
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, float * __restrict__ sum, const int & k0) {
|
||||
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
|
||||
|
||||
GGML_UNUSED(x_qh);
|
||||
|
||||
const int * y_qs = (const int *) y + 4;
|
||||
const half2 * y_ds = (const half2 *) y;
|
||||
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
@ -856,10 +889,9 @@ static __device__ __forceinline__ void vec_dot_q5_K_q8_1_mul_mat(
|
||||
|
||||
const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/16]) + 2 * ((k0 % 16) / 8);
|
||||
|
||||
const int index_x = i * (QR5_K*WARP_SIZE + 1) + QR5_K*k0;
|
||||
const int index_y = j * WARP_SIZE + (QR5_K*k0) % WARP_SIZE;
|
||||
sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q5_K_q8_1_impl_mmq(
|
||||
&x_ql[index_x], &y_qs[index_y], sc, sc+8, x_dm[i * (WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[index_y/QI8_1]);
|
||||
&x_ql[i*(QR5_K*WARP_SIZE + 1) + QR5_K*k0], &y_qs[j*MMQ_TILE_Y_K + (QR5_K*k0) % WARP_SIZE], sc, sc+8,
|
||||
x_dm[i*(WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[j*MMQ_TILE_Y_K + ((QR5_K*k0) % WARP_SIZE)/QI8_1]);
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -932,10 +964,14 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
|
||||
template <int mmq_x, int mmq_y, int nwarps>
|
||||
static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mul_mat(
|
||||
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
||||
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, float * __restrict__ sum, const int & k0) {
|
||||
const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
|
||||
|
||||
GGML_UNUSED(x_qh);
|
||||
|
||||
const float * x_dmf = (const float *) x_dm;
|
||||
const int * y_qs = (const int *) y + 4;
|
||||
const float * y_df = (const float *) y;
|
||||
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
@ -944,15 +980,11 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mul_mat(
|
||||
for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
|
||||
const float * x_dmf = (const float *) x_dm;
|
||||
const float * y_df = (const float *) y_ds;
|
||||
|
||||
const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/8]);
|
||||
|
||||
const int index_x = i * (QR6_K*WARP_SIZE + 1) + QR6_K*k0;
|
||||
const int index_y = j * WARP_SIZE + (QR6_K*k0) % WARP_SIZE;
|
||||
sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q6_K_q8_1_impl_mmq(
|
||||
&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]);
|
||||
&x_ql[i*(QR6_K*WARP_SIZE + 1) + QR6_K*k0], &y_qs[j*MMQ_TILE_Y_K + (QR6_K*k0) % WARP_SIZE], sc,
|
||||
x_dmf[i*(WARP_SIZE/QI6_K) + i/QI6_K], &y_df[j*MMQ_TILE_Y_K + ((QR6_K*k0) % WARP_SIZE)/QI8_1]);
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -964,7 +996,6 @@ struct mmq_type_traits;
|
||||
|
||||
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
|
||||
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_0> {
|
||||
static constexpr bool need_sum = true;
|
||||
static constexpr int vdr = VDR_Q4_0_Q8_1_MMQ;
|
||||
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q4_0<mmq_y, nwarps, need_check>;
|
||||
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q4_0_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
|
||||
@ -972,7 +1003,6 @@ struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_0> {
|
||||
|
||||
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
|
||||
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_1> {
|
||||
static constexpr bool need_sum = true;
|
||||
static constexpr int vdr = VDR_Q4_1_Q8_1_MMQ;
|
||||
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q4_1<mmq_y, nwarps, need_check>;
|
||||
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q4_1_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
|
||||
@ -980,7 +1010,6 @@ struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_1> {
|
||||
|
||||
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
|
||||
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_0> {
|
||||
static constexpr bool need_sum = false;
|
||||
static constexpr int vdr = VDR_Q5_0_Q8_1_MMQ;
|
||||
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q5_0<mmq_y, nwarps, need_check>;
|
||||
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q5_0_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
|
||||
@ -988,7 +1017,6 @@ struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_0> {
|
||||
|
||||
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
|
||||
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_1> {
|
||||
static constexpr bool need_sum = true;
|
||||
static constexpr int vdr = VDR_Q5_1_Q8_1_MMQ;
|
||||
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q5_1<mmq_y, nwarps, need_check>;
|
||||
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q5_1_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
|
||||
@ -996,7 +1024,6 @@ struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_1> {
|
||||
|
||||
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
|
||||
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q8_0> {
|
||||
static constexpr bool need_sum = false;
|
||||
static constexpr int vdr = VDR_Q8_0_Q8_1_MMQ;
|
||||
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q8_0<mmq_y, nwarps, need_check>;
|
||||
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q8_0_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
|
||||
@ -1004,7 +1031,6 @@ struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q8_0> {
|
||||
|
||||
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
|
||||
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q2_K> {
|
||||
static constexpr bool need_sum = false;
|
||||
static constexpr int vdr = VDR_Q2_K_Q8_1_MMQ;
|
||||
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q2_K<mmq_y, nwarps, need_check>;
|
||||
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q2_K_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
|
||||
@ -1012,7 +1038,6 @@ struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q2_K> {
|
||||
|
||||
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
|
||||
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q3_K> {
|
||||
static constexpr bool need_sum = false;
|
||||
static constexpr int vdr = VDR_Q3_K_Q8_1_MMQ;
|
||||
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q3_K<mmq_y, nwarps, need_check>;
|
||||
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q3_K_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
|
||||
@ -1020,7 +1045,6 @@ struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q3_K> {
|
||||
|
||||
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
|
||||
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_K> {
|
||||
static constexpr bool need_sum = true;
|
||||
static constexpr int vdr = VDR_Q4_K_Q8_1_MMQ;
|
||||
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q4_K<mmq_y, nwarps, need_check>;
|
||||
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q4_K_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
|
||||
@ -1028,7 +1052,6 @@ struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_K> {
|
||||
|
||||
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
|
||||
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_K> {
|
||||
static constexpr bool need_sum = true;
|
||||
static constexpr int vdr = VDR_Q5_K_Q8_1_MMQ;
|
||||
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q5_K<mmq_y, nwarps, need_check>;
|
||||
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q5_K_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
|
||||
@ -1036,12 +1059,36 @@ struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_K> {
|
||||
|
||||
template <int mmq_x, int mmq_y, int nwarps, bool need_check>
|
||||
struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q6_K> {
|
||||
static constexpr bool need_sum = false;
|
||||
static constexpr int vdr = VDR_Q6_K_Q8_1_MMQ;
|
||||
static constexpr load_tiles_mmq_t load_tiles = load_tiles_q6_K<mmq_y, nwarps, need_check>;
|
||||
static constexpr vec_dot_mmq_t vec_dot = vec_dot_q6_K_q8_1_mul_mat<mmq_x, mmq_y, nwarps>;
|
||||
};
|
||||
|
||||
static int mmq_need_sum(const ggml_type type_x) {
|
||||
switch (type_x) {
|
||||
case GGML_TYPE_Q4_0:
|
||||
case GGML_TYPE_Q4_1:
|
||||
return true;
|
||||
case GGML_TYPE_Q5_0:
|
||||
return false;
|
||||
case GGML_TYPE_Q5_1:
|
||||
return true;
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
return false;
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
return true;
|
||||
case GGML_TYPE_Q6_K:
|
||||
return false;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
}
|
||||
return false;
|
||||
}
|
||||
|
||||
template <ggml_type type, int mmq_x, int nwarps, bool need_check>
|
||||
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
||||
#if defined(RDNA3) || defined(RDNA2)
|
||||
@ -1056,7 +1103,7 @@ template <ggml_type type, int mmq_x, int nwarps, bool need_check>
|
||||
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
||||
static __global__ void mul_mat_q(
|
||||
const char * __restrict__ x, const char * __restrict__ yc, float * __restrict__ dst,
|
||||
const int ne00, const int ne01, const int stride00, const int ne10, const int ne11, const int ne0) {
|
||||
const int ne00, const int ne01, const int stride01, const int ne10, const int ne11, const int stride11, const int ne0) {
|
||||
|
||||
// Skip unused template specializations for faster compilation:
|
||||
if (mmq_x > get_mmq_x_max_device()) {
|
||||
@ -1068,7 +1115,6 @@ static __global__ void mul_mat_q(
|
||||
constexpr int qr = ggml_cuda_type_traits<type>::qr;
|
||||
constexpr int qi = ggml_cuda_type_traits<type>::qi;
|
||||
constexpr int mmq_y = get_mmq_y_device(mmq_x);
|
||||
constexpr bool need_sum = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::need_sum;
|
||||
constexpr int vdr = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::vdr;
|
||||
constexpr load_tiles_mmq_t load_tiles = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::load_tiles;
|
||||
constexpr vec_dot_mmq_t vec_dot = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::vec_dot;
|
||||
@ -1080,62 +1126,38 @@ static __global__ void mul_mat_q(
|
||||
half2 * tile_x_dm = (half2 *) (tile_x_ql + txs.ql);
|
||||
int * tile_x_qh = (int *) (tile_x_dm + txs.dm);
|
||||
int * tile_x_sc = (int *) (tile_x_qh + txs.qh);
|
||||
int * tile_y_qs = (int *) (tile_x_sc + txs.sc); // [mmq_x * WARP_SIZE]
|
||||
half2 * tile_y_ds = (half2 *) (tile_y_qs + mmq_x*WARP_SIZE); // [mmq_x * WARP_SIZE/QI8_1];
|
||||
|
||||
const block_q8_1 * y = (const block_q8_1 *) yc;
|
||||
int * tile_y = (int *) (tile_x_sc + txs.sc); // [mmq_x * (WARP_SIZE + WARP_SIZE/QI8_1)]
|
||||
|
||||
const int blocks_per_row_x = ne00 / qk;
|
||||
const int blocks_per_col_y = ne10 / QK8_1;
|
||||
const int blocks_per_warp = WARP_SIZE / qi;
|
||||
|
||||
const int & ne1 = ne11;
|
||||
|
||||
const int tile_x_max_i = ne01 - blockIdx.x*mmq_y - 1;
|
||||
|
||||
const int * y = (const int *) yc + blockIdx.y*(mmq_x*sizeof(block_q8_1_mmq)/sizeof(int));
|
||||
|
||||
float sum[(mmq_x/nwarps) * (mmq_y/WARP_SIZE)] = {0.0f};
|
||||
|
||||
for (int kb0 = 0; kb0 < blocks_per_row_x; kb0 += blocks_per_warp) {
|
||||
|
||||
load_tiles(x, tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, stride00*blockIdx.x*mmq_y + kb0, tile_x_max_i, stride00);
|
||||
load_tiles(x, tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, stride01*blockIdx.x*mmq_y + kb0, tile_x_max_i, stride01);
|
||||
|
||||
#pragma unroll
|
||||
for (int kr = 0; kr < qr; ++kr) {
|
||||
const int kqs = kr*WARP_SIZE + threadIdx.x;
|
||||
const int kbxd = kqs / QI8_1;
|
||||
|
||||
const int * by0 = y + stride11*(kb0*(qk*sizeof(block_q8_1_mmq) / (4*QK8_1*sizeof(int))) + kr*sizeof(block_q8_1_mmq)/sizeof(int));
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < mmq_x; i0 += nwarps) {
|
||||
const int i = min(blockIdx.y*mmq_x + threadIdx.y + i0, ne11-1); // to prevent out-of-bounds memory accesses
|
||||
for (int l0 = 0; l0 < mmq_x*MMQ_TILE_Y_K; l0 += nwarps*WARP_SIZE) {
|
||||
int l = l0 + threadIdx.y*WARP_SIZE + threadIdx.x;
|
||||
|
||||
const block_q8_1 * by0 = &y[i*blocks_per_col_y + kb0 * (qk/QK8_1) + kbxd];
|
||||
|
||||
const int index_y = (i0 + threadIdx.y) * WARP_SIZE + kqs % WARP_SIZE;
|
||||
tile_y_qs[index_y] = get_int_from_int8_aligned(by0->qs, threadIdx.x % QI8_1);
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int ids0 = 0; ids0 < mmq_x; ids0 += nwarps * QI8_1) {
|
||||
const int ids = (ids0 + threadIdx.y * QI8_1 + threadIdx.x / (WARP_SIZE/QI8_1)) % mmq_x;
|
||||
const int kby = threadIdx.x % (WARP_SIZE/QI8_1);
|
||||
const int i_y_eff = min(blockIdx.y*mmq_x + ids, ne11-1);
|
||||
|
||||
// if the sum is not needed it's faster to transform the scale to f32 ahead of time
|
||||
const half2 * dsi_src = &y[i_y_eff*blocks_per_col_y + kb0 * (qk/QK8_1) + kr*(WARP_SIZE/QI8_1) + kby].ds;
|
||||
half2 * dsi_dst = &tile_y_ds[ids * (WARP_SIZE/QI8_1) + kby];
|
||||
if (need_sum) {
|
||||
*dsi_dst = *dsi_src;
|
||||
} else {
|
||||
float * dfi_dst = (float *) dsi_dst;
|
||||
*dfi_dst = __low2float(*dsi_src);
|
||||
}
|
||||
tile_y[l] = by0[l];
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// #pragma unroll // unrolling this loop causes too much register pressure
|
||||
for (int k0 = kr*WARP_SIZE/qr; k0 < (kr+1)*WARP_SIZE/qr; k0 += vdr) {
|
||||
vec_dot(tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, tile_y_qs, tile_y_ds, sum, k0);
|
||||
vec_dot(tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, tile_y, sum, k0);
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
@ -1165,8 +1187,8 @@ static __global__ void mul_mat_q(
|
||||
|
||||
struct mmq_args {
|
||||
const char * x; const char * y; float * dst;
|
||||
int64_t ne00; int64_t ne01; int64_t stride00;
|
||||
int64_t ne10; int64_t ne11;
|
||||
int64_t ne00; int64_t ne01; int64_t stride01;
|
||||
int64_t ne10; int64_t ne11; int64_t stride11;
|
||||
int64_t ne0;
|
||||
};
|
||||
|
||||
@ -1184,7 +1206,7 @@ static void launch_mul_mat_q(const mmq_args & args, cudaStream_t stream) {
|
||||
const tile_x_sizes txs = get_tile_x_sizes_host(type, mmq_y);
|
||||
const int shmem_x = txs.ql*sizeof(int) + txs.dm*sizeof(half2) + txs.qh*sizeof(int) + txs.sc*sizeof(int);
|
||||
const int shmem_y = mmq_x*WARP_SIZE*sizeof(int) + mmq_x*(WARP_SIZE/QI8_1)*sizeof(half2);
|
||||
const int shmem = shmem_x + shmem_y;
|
||||
const int shmem = shmem_x + GGML_PAD(shmem_y, nwarps*WARP_SIZE*sizeof(int));
|
||||
|
||||
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
|
||||
static bool shmem_limit_raised[GGML_CUDA_MAX_DEVICES] = {false};
|
||||
@ -1198,11 +1220,11 @@ static void launch_mul_mat_q(const mmq_args & args, cudaStream_t stream) {
|
||||
if (args.ne01 % mmq_y == 0) {
|
||||
const bool need_check = false;
|
||||
mul_mat_q<type, mmq_x, nwarps, need_check><<<block_nums, block_dims, shmem, stream>>>
|
||||
(args.x, args.y, args.dst, args.ne00, args.ne01, args.stride00, args.ne10, args.ne11, args.ne0);
|
||||
(args.x, args.y, args.dst, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0);
|
||||
} else {
|
||||
const bool need_check = true;
|
||||
mul_mat_q<type, mmq_x, nwarps, need_check><<<block_nums, block_dims, shmem, stream>>>
|
||||
(args.x, args.y, args.dst, args.ne00, args.ne01, args.stride00, args.ne10, args.ne11, args.ne0);
|
||||
(args.x, args.y, args.dst, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@ -1,22 +1,23 @@
|
||||
#include "quantize.cuh"
|
||||
#include <cstdint>
|
||||
|
||||
static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int64_t kx, const int64_t kx_padded) {
|
||||
const int64_t ix = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
|
||||
static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int64_t kx, const int64_t kx0_padded) {
|
||||
const int64_t ix0 = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
|
||||
|
||||
if (ix >= kx_padded) {
|
||||
if (ix0 >= kx0_padded) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int64_t iy = (int64_t)blockDim.y*blockIdx.y + threadIdx.y;
|
||||
const int64_t ix1 = blockIdx.y;
|
||||
|
||||
const int64_t i_padded = (int64_t)iy*kx_padded + ix;
|
||||
const int64_t i_padded = ix1*kx0_padded + ix0;
|
||||
|
||||
block_q8_1 * y = (block_q8_1 *) vy;
|
||||
|
||||
const int64_t ib = i_padded / QK8_1; // block index
|
||||
const int64_t iqs = i_padded % QK8_1; // quant index
|
||||
|
||||
const float xi = ix < kx ? x[iy*kx + ix] : 0.0f;
|
||||
const float xi = ix0 < kx ? x[ix1*kx + ix0] : 0.0f;
|
||||
float amax = fabsf(xi);
|
||||
float sum = xi;
|
||||
|
||||
@ -36,10 +37,76 @@ static __global__ void quantize_q8_1(const float * __restrict__ x, void * __rest
|
||||
reinterpret_cast<half&>(y[ib].ds.y) = sum;
|
||||
}
|
||||
|
||||
void quantize_row_q8_1_cuda(const float * x, void * vy, const int64_t kx, const int64_t ky, const int64_t kx_padded, cudaStream_t stream) {
|
||||
const int64_t block_num_x = (kx_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
|
||||
const dim3 num_blocks(block_num_x, ky, 1);
|
||||
const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE, 1, 1);
|
||||
quantize_q8_1<<<num_blocks, block_size, 0, stream>>>(x, vy, kx, kx_padded);
|
||||
template <bool need_sum>
|
||||
static __global__ void quantize_mmq_q8_1(
|
||||
const float * __restrict__ x, void * __restrict__ vy, const int64_t kx0, const int64_t kx1, const int64_t kx0_padded) {
|
||||
|
||||
const int64_t ix0 = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
|
||||
|
||||
if (ix0 >= kx0_padded) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int64_t ix1 = kx1*blockIdx.z + blockIdx.y;
|
||||
|
||||
block_q8_1_mmq * y = (block_q8_1_mmq *) vy;
|
||||
|
||||
const int64_t ib0 = blockIdx.z*(gridDim.y*gridDim.x*blockDim.x/(4*QK8_1)); // first block of channel
|
||||
const int64_t ib = ib0 + (ix0 / (4*QK8_1))*kx1 + blockIdx.y; // block index in channel
|
||||
const int64_t iqs = ix0 % (4*QK8_1); // quant index in block
|
||||
|
||||
const float xi = ix0 < kx0 ? x[ix1*kx0 + ix0] : 0.0f;
|
||||
float amax = fabsf(xi);
|
||||
|
||||
amax = warp_reduce_max(amax);
|
||||
|
||||
float sum;
|
||||
if (need_sum) {
|
||||
sum = warp_reduce_sum(xi);
|
||||
}
|
||||
|
||||
const float d = amax / 127;
|
||||
const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);
|
||||
|
||||
y[ib].qs[iqs] = q;
|
||||
|
||||
if (iqs % QK8_1 != 0) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (need_sum) {
|
||||
y[ib].ds[iqs/QK8_1] = make_half2(d, sum);
|
||||
} else {
|
||||
((float *) y[ib].ds)[iqs/QK8_1] = d;
|
||||
}
|
||||
}
|
||||
|
||||
void quantize_row_q8_1_cuda(
|
||||
const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels,
|
||||
const int64_t kx0_padded, const ggml_type type_x, cudaStream_t stream) {
|
||||
|
||||
GGML_ASSERT(kx0_padded % QK8_1 == 0);
|
||||
|
||||
const int64_t block_num_x = (kx0_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
|
||||
const dim3 num_blocks(block_num_x, kx1*channels, 1);
|
||||
const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE, 1, 1);
|
||||
quantize_q8_1<<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx0_padded);
|
||||
|
||||
GGML_UNUSED(type_x);
|
||||
}
|
||||
|
||||
void quantize_mmq_q8_1_cuda(
|
||||
const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels,
|
||||
const int64_t kx0_padded, const ggml_type type_x, cudaStream_t stream) {
|
||||
|
||||
GGML_ASSERT(kx0_padded % (4*QK8_1) == 0);
|
||||
|
||||
const int64_t block_num_x = (kx0_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
|
||||
const dim3 num_blocks(block_num_x, kx1, channels);
|
||||
const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE, 1, 1);
|
||||
if (mmq_need_sum(type_x)) {
|
||||
quantize_mmq_q8_1<true><<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx1, kx0_padded);
|
||||
} else {
|
||||
quantize_mmq_q8_1<false><<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx1, kx0_padded);
|
||||
}
|
||||
}
|
||||
|
||||
@ -1,5 +1,20 @@
|
||||
#pragma once
|
||||
|
||||
#include "common.cuh"
|
||||
#include "mmq.cuh"
|
||||
|
||||
#include <cstdint>
|
||||
|
||||
#define CUDA_QUANTIZE_BLOCK_SIZE 256
|
||||
|
||||
void quantize_row_q8_1_cuda(const float * x, void * vy, const int64_t kx, const int64_t ky, const int64_t kx_padded, cudaStream_t stream);
|
||||
typedef void (*quantize_cuda_t)(
|
||||
const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels, const int64_t kx0_padded,
|
||||
const ggml_type type_x, cudaStream_t stream);
|
||||
|
||||
void quantize_row_q8_1_cuda(
|
||||
const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels, const int64_t kx0_padded,
|
||||
const ggml_type type_x, cudaStream_t stream);
|
||||
|
||||
void quantize_mmq_q8_1_cuda(
|
||||
const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels, const int64_t kx0_padded,
|
||||
const ggml_type type_x, cudaStream_t stream);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user