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Merge branch 'ggerganov:master' into master

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hsnmkls 2024-02-07 07:31:47 +08:00 • committed by GitHub
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20
README.md
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@ -105,11 +105,14 @@ improved significantly thanks to many contributions. It is the main playground f
- [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417)
- [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553)
- [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi)
- [X] [StableLM-3b-4e1t](https://github.com/ggerganov/llama.cpp/pull/3586)
- [X] [StableLM models](https://huggingface.co/stabilityai)
- [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek)
- [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
- [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557)
- [x] [Phi models](https://huggingface.co/models?search=microsoft/phi)
- [x] [GPT-2](https://huggingface.co/gpt2)
- [x] [Orion 14B](https://github.com/ggerganov/llama.cpp/pull/5118)
- [x] [InternLM2](https://huggingface.co/models?search=internlm2)
- [x] [CodeShell](https://github.com/WisdomShell/codeshell)
**Multimodal models:**
@ -119,6 +122,7 @@ improved significantly thanks to many contributions. It is the main playground f
- [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5)
- [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V)
- [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM)
- [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL)
**Bindings:**
@ -732,9 +736,21 @@ Several quantization methods are supported. They differ in the resulting model d
| 13B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 |
- [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684)
- recent k-quants improvements
- recent k-quants improvements and new i-quants
- [#2707](https://github.com/ggerganov/llama.cpp/pull/2707)
- [#2807](https://github.com/ggerganov/llama.cpp/pull/2807)
- [#4773 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4773)
- [#4856 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4856)
- [#4861 - importance matrix](https://github.com/ggerganov/llama.cpp/pull/4861)
- [#4872 - MoE models](https://github.com/ggerganov/llama.cpp/pull/4872)
- [#4897 - 2-bit quantization](https://github.com/ggerganov/llama.cpp/pull/4897)
- [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930)
- [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957)
- [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969)
- [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996)
- [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060)
- [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196)
- [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361)
### Perplexity (measuring model quality)

448
examples/server/completion.js.hpp
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unsigned int completion_js_len = 5346;
unsigned int completion_js_len = 5782;

3
examples/server/public/completion.js
View file

@ -195,7 +195,8 @@ export const llamaComplete = async (params, controller, callback) => {
// Get the model info from the server. This is useful for getting the context window and so on.
export const llamaModelInfo = async () => {
if (!generation_settings) {
generation_settings = await fetch("/model.json").then(r => r.json());
const props = await fetch("/props").then(r => r.json());
generation_settings = props.default_generation_settings;
}
return generation_settings;
}

11
examples/server/server.cpp
View file

@ -990,11 +990,6 @@ struct llama_server_context
queue_results.send(res);
}
json get_model_props()
{
return get_formated_generation(slots[0]);
}
json get_formated_generation(llama_client_slot &slot)
{
const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
@ -2895,12 +2890,6 @@ int main(int argc, char **argv)
}
});
svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res)
{
const json data = llama.get_model_props();
return res.set_content(data.dump(), "application/json; charset=utf-8");
});
svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res)
{ return res.set_content("", "application/json; charset=utf-8"); });

238
ggml-cuda.cu
View file

@ -5310,41 +5310,50 @@ template <bool need_check> static __global__ void
#endif // __CUDA_ARCH__ >= CC_VOLTA
}
template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows) {
template <int ncols_y_template, int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
static __global__ void mul_mat_vec_q(
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y_par) {
const int ncols_y = ncols_y_template != 0 ? ncols_y_template : ncols_y_par;
const int row = blockIdx.x*blockDim.y + threadIdx.y;
if (row >= nrows) {
if (row >= nrows_x) {
return;
}
const int blocks_per_row = ncols / qk;
const int blocks_per_row_x = ncols_x / qk;
const int blocks_per_col_y = nrows_y / QK8_1;
const int blocks_per_warp = vdr * WARP_SIZE / qi;
// partial sum for each thread
float tmp = 0.0f;
float tmp[ncols_y_template != 0 ? ncols_y_template : 8] = {0.0f};
const block_q_t * x = (const block_q_t *) vx;
const block_q8_1 * y = (const block_q8_1 *) vy;
for (int i = threadIdx.x / (qi/vdr); i < blocks_per_row; i += blocks_per_warp) {
const int ibx = row*blocks_per_row + i; // x block index
for (int i = threadIdx.x / (qi/vdr); i < blocks_per_row_x; i += blocks_per_warp) {
const int ibx = row*blocks_per_row_x + i; // x block index
const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
const int iqs = vdr * (threadIdx.x % (qi/vdr)); // x block quant index when casting the quants to int
tmp += vec_dot_q_cuda(&x[ibx], &y[iby], iqs);
#pragma unroll
for (int j = 0; j < ncols_y; ++j) {
tmp[j] += vec_dot_q_cuda(&x[ibx], &y[j*blocks_per_col_y + iby], iqs);
}
}
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
}
for (int j = 0; j < ncols_y; ++j) {
tmp[j] = warp_reduce_sum(tmp[j]);
if (threadIdx.x == 0) {
dst[row] = tmp;
dst[j*nrows_x + row] = tmp[j];
}
}
}
@ -6816,121 +6825,56 @@ static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, floa
<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
}
static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK4_0 == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot>
static void mul_mat_vec_q_cuda(
const void * vx, const void * vy, float * dst,
const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, cudaStream_t stream) {
static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK4_1 == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
GGML_ASSERT(ncols_x % qk == 0);
GGML_ASSERT(ncols_y <= 4);
static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK5_0 == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const int block_num_y = (nrows_x + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK5_1 == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK8_0 == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_iq2_xxs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI2_XXS, block_iq2_xxs, 1, vec_dot_iq2_xxs_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_iq2_xs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI2_XS, block_iq2_xs, 1, vec_dot_iq2_xs_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_iq3_xxs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % QK_K == 0);
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
switch (ncols_y) {
case 1:
mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
break;
case 2:
mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
break;
case 3:
mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
break;
case 4:
mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
break;
// case 5:
// mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot>
// <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
// break;
// case 6:
// mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot>
// <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
// break;
// case 7:
// mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot>
// <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
// break;
// case 8:
// mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot>
// <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
// break;
default:
GGML_ASSERT(false);
// mul_mat_vec_q<0, qk, qi, block_q_t, vdr, vec_dot>
// <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y);
break;
}
}
static void ggml_mul_mat_q4_0_q8_1_cuda(
@ -8578,50 +8522,61 @@ static void ggml_cuda_op_mul_mat_vec_q(
const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
const int64_t src1_padded_row_size, cudaStream_t stream) {
GGML_ASSERT(ggml_nrows(src1) == 1);
const int64_t ne00 = src0->ne[0];
const int64_t row_diff = row_high - row_low;
switch (src0->type) {
case GGML_TYPE_Q4_0:
mul_mat_vec_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break;
case GGML_TYPE_Q4_1:
mul_mat_vec_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK4_1, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break;
case GGML_TYPE_Q5_0:
mul_mat_vec_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break;
case GGML_TYPE_Q5_1:
mul_mat_vec_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break;
case GGML_TYPE_Q8_0:
mul_mat_vec_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break;
case GGML_TYPE_Q2_K:
mul_mat_vec_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break;
case GGML_TYPE_Q3_K:
mul_mat_vec_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break;
case GGML_TYPE_Q4_K:
mul_mat_vec_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break;
case GGML_TYPE_Q5_K:
mul_mat_vec_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break;
case GGML_TYPE_Q6_K:
mul_mat_vec_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break;
case GGML_TYPE_IQ2_XXS:
mul_mat_vec_iq2_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK_K, QI2_XXS, block_iq2_xxs, 1, vec_dot_iq2_xxs_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break;
case GGML_TYPE_IQ2_XS:
mul_mat_vec_iq2_xs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK_K, QI2_XS, block_iq2_xs, 1, vec_dot_iq2_xs_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break;
case GGML_TYPE_IQ3_XXS:
mul_mat_vec_iq3_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q_cuda<QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream);
break;
default:
GGML_ASSERT(false);
@ -9945,17 +9900,18 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
#ifdef GGML_CUDA_FORCE_DMMV
const bool use_mul_mat_vec_q = false;
#else
const bool use_mul_mat_vec_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type) && ggml_nrows(src1) == 1;
const bool use_mul_mat_vec_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type);
#endif // GGML_CUDA_FORCE_DMMV
if (use_mul_mat_vec_q) {
// NOTE: this kernel does not support ggml_nrows(src1) > 1
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true);
} else {
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false);
}
} else {
if (use_mul_mat_q) {
if (src1->ne[1] <= 4 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type)) {
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true);
} else if (use_mul_mat_q) {
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true);
} else {
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);

67
ggml-quants.c
View file

@ -2381,7 +2381,10 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri
uint8_t L[QK_K];
uint8_t Laux[32];
uint8_t Ls[QK_K/32];
uint8_t Lm[QK_K/32];
float weights[32];
float sw[QK_K/32];
float mins[QK_K/32];
float scales[QK_K/32];
@ -2389,11 +2392,9 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri
float sum_x2 = 0;
for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l];
float sigma2 = sum_x2/QK_K;
float sigma2 = 2*sum_x2/QK_K;
float av_x = sqrtf(sigma2);
float max_scale = 0; // as we are deducting the min, scales are always positive
float max_min = 0;
for (int j = 0; j < QK_K/32; ++j) {
if (quant_weights) {
const float * qw = quant_weights + QK_K*i + 32*j;
@ -2401,25 +2402,17 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri
} else {
for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
}
float sumw = 0;
for (int l = 0; l < 32; ++l) sumw += weights[l];
sw[j] = sumw;
scales[j] = make_qkx3_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false);
//scales[j] = make_qkx2_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -1.f, 0.1f, 20, false);
float scale = scales[j];
if (scale > max_scale) {
max_scale = scale;
}
float min = mins[j];
if (min > max_min) {
max_min = min;
}
}
float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f;
float inv_min = max_min > 0 ? 63.f/max_min : 0.f;
float d_block = make_qp_quants(QK_K/32, 63, scales, Ls, sw);
float m_block = make_qp_quants(QK_K/32, 63, mins, Lm, sw);
for (int j = 0; j < QK_K/32; ++j) {
uint8_t ls = nearest_int(inv_scale*scales[j]);
uint8_t lm = nearest_int(inv_min*mins[j]);
ls = MIN(63, ls);
lm = MIN(63, lm);
uint8_t ls = Ls[j];
uint8_t lm = Lm[j];
if (j < 4) {
y[i].scales[j] = ls;
y[i].scales[j+4] = lm;
@ -2429,8 +2422,8 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri
y[i].scales[j-0] |= ((lm >> 4) << 6);
}
}
y[i].d = GGML_FP32_TO_FP16(max_scale/63.f);
y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f);
y[i].d = GGML_FP32_TO_FP16(d_block);
y[i].dmin = GGML_FP32_TO_FP16(m_block);
uint8_t sc, m;
for (int j = 0; j < QK_K/32; ++j) {
@ -2688,20 +2681,21 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri
const int nb = n_per_row / QK_K;
uint8_t L[QK_K];
uint8_t Laux[32];
uint8_t Ls[QK_K/32];
uint8_t Lm[QK_K/32];
float mins[QK_K/32];
float scales[QK_K/32];
float sw[QK_K/32];
float weights[32];
uint8_t Laux[32];
for (int i = 0; i < nb; i++) {
float sum_x2 = 0;
for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l];
float sigma2 = sum_x2/QK_K;
float sigma2 = 2*sum_x2/QK_K;
float av_x = sqrtf(sigma2);
float max_scale = 0; // as we are deducting the min, scales are always positive
float max_min = 0;
for (int j = 0; j < QK_K/32; ++j) {
if (quant_weights) {
const float * qw = quant_weights + QK_K*i + 32*j;
@ -2709,22 +2703,19 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri
} else {
for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
}
float sumw = 0;
for (int l = 0; l < 32; ++l) sumw += weights[l];
sw[j] = sumw;
scales[j] = make_qkx3_quants(32, 31, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false);
float scale = scales[j];
if (scale > max_scale) {
max_scale = scale;
}
float min = mins[j];
if (min > max_min) {
max_min = min;
}
}
float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f;
float inv_min = max_min > 0 ? 63.f/max_min : 0.f;
float d_block = make_qp_quants(QK_K/32, 63, scales, Ls, sw);
float m_block = make_qp_quants(QK_K/32, 63, mins, Lm, sw);
for (int j = 0; j < QK_K/32; ++j) {
uint8_t ls = nearest_int(inv_scale*scales[j]);
uint8_t lm = nearest_int(inv_min*mins[j]);
uint8_t ls = Ls[j];
uint8_t lm = Lm[j];
ls = MIN(63, ls);
lm = MIN(63, lm);
if (j < 4) {
@ -2736,8 +2727,8 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri
y[i].scales[j-0] |= ((lm >> 4) << 6);
}
}
y[i].d = GGML_FP32_TO_FP16(max_scale/63.f);
y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f);
y[i].d = GGML_FP32_TO_FP16(d_block);
y[i].dmin = GGML_FP32_TO_FP16(m_block);
uint8_t sc, m;
for (int j = 0; j < QK_K/32; ++j) {