mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-03 09:22:01 +00:00
llama : add gpt-oss (#15091)
* oai moe * compat with new checkpoint * add attn sink impl * add rope scaling yarn * logits match with latest transformers code * wip chat template * rm trailing space * use ggml_scale_bias * rm redundant is_swa_all * convert interleaved gate_up * graph : fix activation function to match reference (#7) * vocab : handle o200k_harmony special tokens * ggml : add attention sinks support (#1) * llama : add attn sinks * ggml : add attn sinks * cuda : add attn sinks * vulkan : add support for sinks in softmax remove unnecessary return * ggml : add fused swiglu_oai op (#11) * ggml : add fused swiglu_oai op * Update ggml/src/ggml-cpu/ops.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update CUDA impl * cont : metal impl * add vulkan impl * test-backend-ops : more test cases, clean up * llama : remove unfused impl * remove extra lines --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: slaren <slarengh@gmail.com> * repack mxfp4 upon conversion * clean up a bit * enable thinking * add quick hack to render only some special tokens * fix bf16 conversion * remove vocab hack * webui ok * support chat parsing for gpt-oss * fix webui * direct mapping mxfp4, FINALLY * force using mxfp4 * properly use lazy tensor * ggml : add mxfp4 ggml : use e8m0 conversion instead of powf Co-authored-by: Diego Devesa <slarengh@gmail.com> change kvalues_mxfp4 table to match e2m1 (#6) metal : remove quantization for now (not used) cuda : fix disabled CUDA graphs due to ffn moe bias vulkan : add support for mxfp4 cont : add cm2 dequant * ggml : add ggml_add_id (#13) * ggml : add ggml_add_id * add cuda impl * llama : add weight support check for add_id * perf opt * add vulkan impl * rename cuda files * add metal impl * allow in-place ggml_add_id * llama : keep biases on CPU with --cpu-moe * llama : fix compile error ggml-ci * cuda : add fallback for __nv_cvt_e8m0_to_bf16raw ggml-ci * cleanup ggml-ci * sycl : fix supports_op for MXFP4 ggml-ci * fix Unknown reasoning format * ggml-cpu : fix AVX build ggml-ci * fix hip build ggml-ci * cuda : add mxfp4 dequantization support for cuBLAS ggml-ci * ggml-cpu : fix mxfp4 fallback definitions for some architectures ggml-ci * cuda : fix version required for __nv_cvt_e8m0_to_bf16raw --------- Co-authored-by: Xuan Son Nguyen <son@huggingface.co> Co-authored-by: slaren <slarengh@gmail.com>
This commit is contained in:
Georgi Gerganov
@@ -1,8 +1,20 @@
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#pragma once
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#include "common.cuh"
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#include <cstdint>
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static __device__ __forceinline__ int get_int_b1(const void * x, const int & i32) {
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const uint8_t * x8 = (const uint8_t *) x;
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int x32 = x8[4*i32 + 0] << 0;
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x32 |= x8[4*i32 + 1] << 8;
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x32 |= x8[4*i32 + 2] << 16;
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x32 |= x8[4*i32 + 3] << 24;
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return x32;
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}
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static __device__ __forceinline__ int get_int_b2(const void * x, const int & i32) {
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const uint16_t * x16 = (const uint16_t *) x; // assume at least 2 byte alignment
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@@ -16,6 +28,20 @@ static __device__ __forceinline__ int get_int_b4(const void * x, const int & i32
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return ((const int *) x)[i32]; // assume at least 4 byte alignment
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}
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static __device__ __forceinline__ int2 get_int_from_table_16(const int & q4, const int8_t * table) {
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const int q0_32 = (q4 >> 0) & 0x0F0F0F0F;
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const int8_t * q0_8 = (const int8_t *) &q0_32;
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const char4 val0_8 = make_char4(
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table[q0_8[0]], table[q0_8[1]], table[q0_8[2]], table[q0_8[3]]);
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const int q1_32 = (q4 >> 4) & 0x0F0F0F0F;
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const int8_t * q1_8 = (const int8_t *) &q1_32;
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const char4 val1_8 = make_char4(
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table[q1_8[0]], table[q1_8[1]], table[q1_8[2]], table[q1_8[3]]);
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return make_int2(*((const int *) &val0_8), *((const int *) &val1_8));
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}
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// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
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// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
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@@ -211,6 +237,30 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q8_0_16_q8_1_
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return d8_1*sumf;
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}
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#define VDR_MXFP4_Q8_1_MMVQ 2
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#define VDR_MXFP4_Q8_1_MMQ 4
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static __device__ __forceinline__ float vec_dot_mxfp4_q8_1(
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const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
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const block_mxfp4 * bq4 = (const block_mxfp4 *) vbq + kbx;
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const int * q8 = (const int *) bq8_1->qs + iqs;
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int sumi = 0;
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#pragma unroll
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for (int l = 0; l < VDR_MXFP4_Q8_1_MMVQ; ++l) {
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const int aux_q4 = get_int_b1(bq4->qs, iqs + l);
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const int2 v = get_int_from_table_16(aux_q4, kvalues_mxfp4);
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sumi = ggml_cuda_dp4a(v.x, q8[l + 0], sumi);
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sumi = ggml_cuda_dp4a(v.y, q8[l + 4], sumi);
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}
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const float d = ggml_cuda_e8m0_to_fp32(bq4->e) * 0.5f * __low2float(bq8_1->ds);
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return d * sumi;
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}
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#define VDR_Q2_K_Q8_1_MMVQ 1
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#define VDR_Q2_K_Q8_1_MMQ 4
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@@ -1068,20 +1118,6 @@ static __device__ __forceinline__ float vec_dot_iq1_m_q8_1(
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return d * ((sumi[0] + sumf[0]) * sc0 + (sumi[1] + sumf[1]) * sc1);
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}
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static __device__ __forceinline__ int2 get_int_from_table_16(const int & q4) {
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const int q0_32 = (q4 >> 0) & 0x0F0F0F0F;
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const int8_t * q0_8 = (const int8_t *) &q0_32;
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const char4 val0_8 = make_char4(
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kvalues_iq4nl[q0_8[0]], kvalues_iq4nl[q0_8[1]], kvalues_iq4nl[q0_8[2]], kvalues_iq4nl[q0_8[3]]);
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const int q1_32 = (q4 >> 4) & 0x0F0F0F0F;
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const int8_t * q1_8 = (const int8_t *) &q1_32;
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const char4 val1_8 = make_char4(
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kvalues_iq4nl[q1_8[0]], kvalues_iq4nl[q1_8[1]], kvalues_iq4nl[q1_8[2]], kvalues_iq4nl[q1_8[3]]);
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return make_int2(*((const int *) &val0_8), *((const int *) &val1_8));
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}
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#define VDR_IQ4_NL_Q8_1_MMVQ 2
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#define VDR_IQ4_NL_Q8_1_MMQ 4
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@@ -1096,7 +1132,7 @@ static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1(
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#pragma unroll
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for (int l = 0; l < VDR_Q4_0_Q8_1_MMVQ; ++l) {
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const int aux_q4 = get_int_b2(bq4->qs, iqs + l);
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const int2 v = get_int_from_table_16(aux_q4);
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const int2 v = get_int_from_table_16(aux_q4, kvalues_iq4nl);
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sumi = ggml_cuda_dp4a(v.x, q8[l + 0], sumi);
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sumi = ggml_cuda_dp4a(v.y, q8[l + 4], sumi);
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@@ -1118,7 +1154,7 @@ static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1(
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#pragma unroll
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for (int j = 0; j < 4; ++j) {
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const int aux_q4 = get_int_b4(bq4->qs, iqs + j);
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const int2 v = get_int_from_table_16(aux_q4);
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const int2 v = get_int_from_table_16(aux_q4, kvalues_iq4nl);
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const int u0 = get_int_b4(bq8_1[iqs/4].qs, j + 0);
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const int u1 = get_int_b4(bq8_1[iqs/4].qs, j + 4);
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