mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 02:44:36 +00:00
CLBlast: Add outer loops over src0 for broadcasting in mulmat
Reduce repeated dequantization of the same data.
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shibe2
parent
d1031cf49c
commit
465219b914
@ -1489,23 +1489,20 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
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cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
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size_t x_offset = 0;
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int64_t pi02 = -1;
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int64_t pi03 = -1;
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for (int64_t i13 = 0; i13 < ne13; i13++) {
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int64_t i03 = i13 / r3;
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for (int64_t i12 = 0; i12 < ne12; i12++) {
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int64_t i02 = i12 / r2;
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// copy data to device
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for (int64_t i03 = 0; i03 < ne03; i03++) {
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// TODO: copy src0 here when r3>1
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for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
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for (int64_t i02 = 0; i02 < ne02; i02++) {
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if (src0->backend == GGML_BACKEND_GPU) {
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x_offset = (i03 * ne02 + i02) * x_ne;
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} else if (i02 != pi02 || i03 != pi03) {
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} else {
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// copy src0 to device
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CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
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pi02 = i02;
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pi03 = i03;
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}
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for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
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// copy src1 to device
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CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
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CL_CHECK(clFinish(queue));
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@ -1531,6 +1528,8 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
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CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL));
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}
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}
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}
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}
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if (src0->backend != GGML_BACKEND_GPU) {
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ggml_cl_pool_free(d_X, x_size);
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@ -1589,24 +1588,19 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
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bool src1_cont_cols = (size_t)nb11 == ne11*sizeof(float);
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size_t x_offset = 0;
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int64_t pi02 = -1;
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int64_t pi03 = -1;
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for (int64_t i13 = 0; i13 < ne13; i13++) {
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int64_t i03 = i13 / r3;
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for (int64_t i12 = 0; i12 < ne12; i12++) {
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int64_t i02 = i12 / r2;
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// copy src0 to device
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for (int64_t i03 = 0; i03 < ne03; i03++) {
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// TODO: copy src0 here when r3>1
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for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
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for (int64_t i02 = 0; i02 < ne02; i02++) {
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if (src0->backend == GGML_BACKEND_GPU) {
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x_offset = (i03 * ne02 + i02) * x_ne;
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} else if (i02 != pi02 || i03 != pi03) {
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} else {
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// copy src0 to device
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CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
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pi02 = i02;
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pi03 = i03;
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}
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for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
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// convert src1 to fp16
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// TODO: use multiple threads
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char * src1i = (char *) src1->data + i13*nb13 + i12*nb12;
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@ -1658,6 +1652,8 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
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ggml_fp16_to_fp32_row(tmp, d, d_ne);
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}
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}
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}
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}
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if (src0->backend != GGML_BACKEND_GPU) {
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ggml_cl_pool_free(d_X, x_size);
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@ -1718,28 +1714,30 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
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size_t ev_idx = 0;
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std::vector<cl_event> events;
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int64_t pi02 = -1;
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int64_t pi03 = -1;
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for (int64_t i13 = 0; i13 < ne13; i13++) {
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int64_t i03 = i13 / r3;
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for (int64_t i12 = 0; i12 < ne12; i12++) {
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int64_t i02 = i12 / r2;
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for (int64_t i03 = 0; i03 < ne03; i03++) {
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// TODO: copy and dequantize src0 here when r3>1
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for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
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for (int64_t i02 = 0; i02 < ne02; i02++) {
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// copy src0 to device if necessary
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if (src0->backend == GGML_BACKEND_CPU) {
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if (i02 != pi02 || i03 != pi03) {
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events.emplace_back();
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CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++));
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pi02 = i02;
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pi03 = i03;
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}
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} else if (src0->backend == GGML_BACKEND_GPU) {
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d_Q = (cl_mem) src0->extra;
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} else {
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GGML_ASSERT(false);
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}
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if (!mul_mat_vec) {
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// convert src0 to fp32 on device
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const size_t global = x_ne / global_denom;
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const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0;
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CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q));
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CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X));
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CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, &offset, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));
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}
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for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
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if (mul_mat_vec) { // specialized dequantize_mul_mat_vec kernel
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// copy src1 to device
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events.emplace_back();
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@ -1756,23 +1754,15 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
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CL_CHECK(clSetKernelArg(*dmmv, 3, sizeof(cl_mem), &d_D));
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CL_CHECK(clSetKernelArg(*dmmv, 4, sizeof(cl_int), &ncols));
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CL_CHECK(clEnqueueNDRangeKernel(queue, *dmmv, 1, &offset, &global, &local, events.size() - 1, events.data(), events.data() + ev_idx++));
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} else { // general dequantization kernel + CLBlast matrix matrix multiplication
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// convert src0 to fp32 on device
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const size_t global = x_ne / global_denom;
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const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0;
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CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q));
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CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X));
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CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, offset > 0 ? &offset : NULL, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));
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} else { // CLBlast matrix matrix multiplication
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// copy src1 to device
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CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
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events.emplace_back();
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// wait for conversion
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CL_CHECK(clFinish(queue));
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// compute
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events.emplace_back();
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clblast::StatusCode status = clblast::Gemm<cl_float>(clblast::Layout::kColMajor,
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clblast::Transpose::kYes, clblast::Transpose::kNo,
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ne01, ne11, ne10,
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@ -1799,6 +1789,8 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
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events.clear();
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}
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}
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}
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}
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if (!mul_mat_vec) {
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ggml_cl_pool_free(d_X, x_size);
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