vbatts/llama.cpp
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Merge branch 'ggerganov:master' into master

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Randall Fitzgerald 2023-06-12 16:18:13 -04:00 • committed by GitHub
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16 changed files with 565 additions and 264 deletions
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4
.github/workflows/build.yml vendored
View file

@ -10,10 +10,10 @@ on:
push:
branches:
- master
paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp']
paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu']
pull_request:
types: [opened, synchronize, reopened]
paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp']
paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu']
env:
BRANCH_NAME: ${{ github.head_ref || github.ref_name }}

3
CMakeLists.txt
View file

@ -432,6 +432,9 @@ target_link_libraries(llama PRIVATE
if (BUILD_SHARED_LIBS)
set_target_properties(llama PROPERTIES POSITION_INDEPENDENT_CODE ON)
target_compile_definitions(llama PRIVATE LLAMA_SHARED LLAMA_BUILD)
if (LLAMA_METAL)
set_target_properties(llama PROPERTIES RESOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal")
endif()
endif()
if (GGML_SOURCES_CUDA)

2
README.md
View file

@ -308,7 +308,7 @@ Building the program with BLAS support may lead to some performance improvements
- #### BLIS
Check [BLIS.md](BLIS.md) for more information.
Check [BLIS.md](docs/BLIS.md) for more information.
- #### Intel MKL

8
SHA256SUMS
View file

@ -1,6 +1,6 @@
700df0d3013b703a806d2ae7f1bfb8e59814e3d06ae78be0c66368a50059f33d models/7B/consolidated.00.pth
666a4bb533b303bdaf89e1b6a3b6f93535d868de31d903afdc20983dc526c847 models/7B/ggml-model-f16.bin
ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/7B/ggml-model-q4_0.bin
ec2f2d1f0dfb73b72a4cbac7fa121abbe04c37ab327125a38248f930c0f09ddf models/7B/ggml-model-q4_0.bin
ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/7B/ggml-model-q4_1.bin
ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/7B/ggml-model-q5_0.bin
ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/7B/ggml-model-q5_1.bin
@ -8,7 +8,7 @@ ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/7B/ggml
745bf4e29a4dd6f411e72976d92b452da1b49168a4f41c951cfcc8051823cf08 models/13B/consolidated.00.pth
d5ccbcc465c71c0de439a5aeffebe8344c68a519bce70bc7f9f92654ee567085 models/13B/consolidated.01.pth
2b206e9b21fb1076f11cafc624e2af97c9e48ea09312a0962153acc20d45f808 models/13B/ggml-model-f16.bin
ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/13B/ggml-model-q4_0.bin
fad169e6f0f575402cf75945961cb4a8ecd824ba4da6be2af831f320c4348fa5 models/13B/ggml-model-q4_0.bin
ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/13B/ggml-model-q4_1.bin
ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/13B/ggml-model-q5_0.bin
ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/13B/ggml-model-q5_1.bin
@ -18,7 +18,7 @@ e23294a58552d8cdec5b7e8abb87993b97ea6eced4178ff2697c02472539d067 models/30B/con
24a87f01028cbd3a12de551dcedb712346c0b5cbdeff1454e0ddf2df9b675378 models/30B/consolidated.02.pth
1adfcef71420886119544949767f6a56cb6339b4d5fcde755d80fe68b49de93b models/30B/consolidated.03.pth
7e1b524061a9f4b27c22a12d6d2a5bf13b8ebbea73e99f218809351ed9cf7d37 models/30B/ggml-model-f16.bin
ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/30B/ggml-model-q4_0.bin
d2a441403944819492ec8c2002cc36fa38468149bfb4b7b4c52afc7bd9a7166d models/30B/ggml-model-q4_0.bin
ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/30B/ggml-model-q4_1.bin
ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/30B/ggml-model-q5_0.bin
ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/30B/ggml-model-q5_1.bin
@ -32,7 +32,7 @@ a287c0dfe49081626567c7fe87f74cce5831f58e459b427b5e05567641f47b78 models/65B/con
72b4eba67a1a3b18cb67a85b70f8f1640caae9b40033ea943fb166bd80a7b36b models/65B/consolidated.06.pth
d27f5b0677d7ff129ceacd73fd461c4d06910ad7787cf217b249948c3f3bc638 models/65B/consolidated.07.pth
60758f2384d74e423dffddfd020ffed9d3bb186ebc54506f9c4a787d0f5367b0 models/65B/ggml-model-f16.bin
ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/65B/ggml-model-q4_0.bin
cde053439fa4910ae454407e2717cc46cc2c2b4995c00c93297a2b52e790fa92 models/65B/ggml-model-q4_0.bin
ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/65B/ggml-model-q4_1.bin
ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/65B/ggml-model-q5_0.bin
ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/65B/ggml-model-q5_1.bin

3
examples/common.cpp
View file

@ -632,6 +632,9 @@ void console_set_color(console_state & con_st, console_color_t color) {
case CONSOLE_COLOR_USER_INPUT:
fprintf(con_st.out, ANSI_BOLD ANSI_COLOR_GREEN);
break;
case CONSOLE_COLOR_ERROR:
fprintf(con_st.out, ANSI_BOLD ANSI_COLOR_RED);
break;
}
con_st.color = color;
fflush(con_st.out);

3
examples/common.h
View file

@ -112,7 +112,8 @@ struct llama_context * llama_init_from_gpt_params(const gpt_params & params);
enum console_color_t {
CONSOLE_COLOR_DEFAULT=0,
CONSOLE_COLOR_PROMPT,
CONSOLE_COLOR_USER_INPUT
CONSOLE_COLOR_USER_INPUT,
CONSOLE_COLOR_ERROR
};
struct console_state {

16
examples/main/main.cpp
View file

@ -81,6 +81,9 @@ int main(int argc, char ** argv) {
if (params.n_ctx > 2048) {
fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
"expect poor results\n", __func__, params.n_ctx);
} else if (params.n_ctx < 8) {
fprintf(stderr, "%s: warning: minimum context size is 8, using minimum size.\n", __func__);
params.n_ctx = 8;
}
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
@ -331,6 +334,19 @@ int main(int argc, char ** argv) {
while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
// predict
if (embd.size() > 0) {
// Note: n_ctx - 4 here is to match the logic for commandline prompt handling via
// --prompt or --file which uses the same value.
auto max_embd_size = n_ctx - 4;
// Ensure the input doesn't exceed the context size by truncating embd if necessary.
if ((int)embd.size() > max_embd_size) {
auto skipped_tokens = embd.size() - max_embd_size;
console_set_color(con_st, CONSOLE_COLOR_ERROR);
printf("<<input too long: skipped %ld token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
fflush(stdout);
embd.resize(max_embd_size);
}
// infinite text generation via context swapping
// if we run out of context:
// - take the n_keep first tokens from the original prompt (via n_past)

2
flake.nix
View file

@ -28,7 +28,7 @@
postPatch =
if isM1 then ''
substituteInPlace ./ggml-metal.m \
--replace '[[NSBundle mainBundle] pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/ggml-metal.metal\";"
--replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/ggml-metal.metal\";"
'' else "";
nativeBuildInputs = with pkgs; [ cmake ];
buildInputs = osSpecific;

26
ggml-cuda.cu
View file

@ -1105,6 +1105,9 @@ void * ggml_cuda_host_malloc(size_t size) {
void * ptr = nullptr;
cudaError_t err = cudaMallocHost((void **) &ptr, size);
if (err != cudaSuccess) {
// The allocation error can be bypassed. A null ptr will assigned out of this function.
// This can fixed the OOM error in WSL.
cudaGetLastError();
fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
size/1024.0/1024.0, cudaGetErrorString(err));
return nullptr;
@ -1710,8 +1713,7 @@ void ggml_cuda_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tens
(void) dst;
}
void ggml_cuda_load_data(const char * fname, struct ggml_tensor * tensor, const size_t offset) {
FILE * fp = fopen(fname, "rb");
void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) {
int nrows = ggml_nrows(tensor);
const size_t nb1 = tensor->nb[1];
ggml_backend backend = tensor->backend;
@ -1745,35 +1747,19 @@ void ggml_cuda_load_data(const char * fname, struct ggml_tensor * tensor, const
int64_t nrows_split = row_high - row_low;
const size_t offset_split = offset + row_low*nb1;
const size_t offset_split = row_low*nb1;
const size_t size = ggml_nbytes_split(tensor, nrows_split);
void * buf;
CUDA_CHECK(cudaMalloc(&buf, size));
void * buf_host = malloc(size);
#ifdef _WIN32
int ret = _fseeki64(fp, (__int64) offset_split, SEEK_SET);
#else
int ret = fseek(fp, (long) offset_split, SEEK_SET);
#endif
GGML_ASSERT(ret == 0); // same
size_t ret2 = fread(buf_host, size, 1, fp);
if (ret2 != 1) {
fprintf(stderr, "unexpectedly reached end of file");
exit(1);
}
void * buf_host = (char*)data + offset_split;
cudaMemcpy(buf, buf_host, size, cudaMemcpyHostToDevice);
cudaDeviceSynchronize();
free(buf_host);
extra->data_device[id] = buf;
}
tensor->extra = extra;
fclose(fp);
}
void ggml_cuda_free_data(struct ggml_tensor * tensor) {

3
ggml-cuda.h
View file

@ -24,7 +24,8 @@ void ggml_cuda_mul_mat(const struct ggml_tensor * src0, const struct ggml_tens
void * ggml_cuda_host_malloc(size_t size);
void ggml_cuda_host_free(void * ptr);
void ggml_cuda_load_data(const char * fname, struct ggml_tensor * tensors, size_t offset);
void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
void ggml_cuda_free_data(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
void ggml_cuda_set_main_device(int main_device);

51
ggml-metal.m
View file

@ -52,14 +52,18 @@ struct ggml_metal_context {
GGML_METAL_DECL_KERNEL(get_rows_q4_0);
GGML_METAL_DECL_KERNEL(get_rows_q4_1);
GGML_METAL_DECL_KERNEL(get_rows_q2_k);
GGML_METAL_DECL_KERNEL(get_rows_q3_k);
GGML_METAL_DECL_KERNEL(get_rows_q4_k);
GGML_METAL_DECL_KERNEL(get_rows_q5_k);
GGML_METAL_DECL_KERNEL(get_rows_q6_k);
GGML_METAL_DECL_KERNEL(rms_norm);
GGML_METAL_DECL_KERNEL(mul_mat_f16_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q4_1_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q2_k_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q3_k_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q4_k_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q5_k_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q6_k_f32);
GGML_METAL_DECL_KERNEL(rope);
GGML_METAL_DECL_KERNEL(cpy_f32_f16);
@ -73,6 +77,12 @@ struct ggml_metal_context {
// for now it is easier to work in a separate file
static NSString * const msl_library_source = @"see metal.metal";
// Here to assist with NSBundle Path Hack
@interface GGMLMetalClass : NSObject
@end
@implementation GGMLMetalClass
@end
struct ggml_metal_context * ggml_metal_init(void) {
fprintf(stderr, "%s: allocating\n", __func__);
@ -80,6 +90,7 @@ struct ggml_metal_context * ggml_metal_init(void) {
ctx->device = MTLCreateSystemDefaultDevice();
ctx->queue = [ctx->device newCommandQueue];
ctx->n_buffers = 0;
// determine if we can use MPS
if (MPSSupportsMTLDevice(ctx->device)) {
@ -108,7 +119,8 @@ struct ggml_metal_context * ggml_metal_init(void) {
NSError * error = nil;
//NSString * path = [[NSBundle mainBundle] pathForResource:@"../../examples/metal/metal" ofType:@"metal"];
NSString * path = [[NSBundle mainBundle] pathForResource:@"ggml-metal" ofType:@"metal"];
NSBundle * bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
NSString * path = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
fprintf(stderr, "%s: loading '%s'\n", __func__, [path UTF8String]);
NSString * src = [NSString stringWithContentsOfFile:path encoding:NSUTF8StringEncoding error:&error];
@ -145,14 +157,18 @@ struct ggml_metal_context * ggml_metal_init(void) {
GGML_METAL_ADD_KERNEL(get_rows_q4_0);
GGML_METAL_ADD_KERNEL(get_rows_q4_1);
GGML_METAL_ADD_KERNEL(get_rows_q2_k);
GGML_METAL_ADD_KERNEL(get_rows_q3_k);
GGML_METAL_ADD_KERNEL(get_rows_q4_k);
GGML_METAL_ADD_KERNEL(get_rows_q5_k);
GGML_METAL_ADD_KERNEL(get_rows_q6_k);
GGML_METAL_ADD_KERNEL(rms_norm);
GGML_METAL_ADD_KERNEL(mul_mat_f16_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q4_1_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q2_k_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q3_k_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q4_k_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q5_k_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q6_k_f32);
GGML_METAL_ADD_KERNEL(rope);
GGML_METAL_ADD_KERNEL(cpy_f32_f16);
@ -567,6 +583,15 @@ void ggml_metal_graph_compute(
nth1 = 16;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q2_k_f32];
} break;
case GGML_TYPE_Q3_K:
{
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 4;
nth1 = 16;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q3_k_f32];
} break;
case GGML_TYPE_Q4_K:
{
GGML_ASSERT(ne02 == 1);
@ -576,6 +601,15 @@ void ggml_metal_graph_compute(
nth1 = 16;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_k_f32];
} break;
case GGML_TYPE_Q5_K:
{
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 4;
nth1 = 16;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q5_k_f32];
} break;
case GGML_TYPE_Q6_K:
{
GGML_ASSERT(ne02 == 1);
@ -612,15 +646,14 @@ void ggml_metal_graph_compute(
if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1) {
[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} else if (src0t == GGML_TYPE_Q2_K) {
}
else if (src0t == GGML_TYPE_Q2_K ||
src0t == GGML_TYPE_Q3_K ||
src0t == GGML_TYPE_Q4_K ||
src0t == GGML_TYPE_Q5_K ||
src0t == GGML_TYPE_Q6_K) {
[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} else if (src0t == GGML_TYPE_Q4_K) {
[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} else if (src0t == GGML_TYPE_Q6_K) {
[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} else {
[encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
@ -638,7 +671,9 @@ void ggml_metal_graph_compute(
case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break;
case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_1]; break;
case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q2_k]; break;
case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q3_k]; break;
case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_k]; break;
case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_k]; break;
case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_k]; break;
default: GGML_ASSERT(false && "not implemented");
}

547
ggml-metal.metal
View file

@ -304,34 +304,22 @@ kernel void kernel_mul_mat_q4_0_f32(
device const float * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant int64_t & ne11,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
threadgroup float * sum [[threadgroup(0)]],
uint2 tgpig[[threadgroup_position_in_grid]],
uint2 tpig[[thread_position_in_grid]],
uint2 tpitg[[thread_position_in_threadgroup]],
uint2 tptg[[threads_per_threadgroup]]) {
const int nb = ne00/QK4_0;
const int8_t m8 = 8;
const int64_t r0 = tgpig.x;
const int64_t r1 = tgpig.y;
device const block_q4_0 * x = (device const block_q4_0 *) src0 + r0*nb;
device const float * y = (device const float *) src1 + r1*ne10;
const uint nth = tptg.x*tptg.y;
const uint ith = tptg.y*tpitg.x + tpitg.y;
const int nth = tptg.x*tptg.y;
const int ith = tptg.y*tpitg.x + tpitg.y;
const int ix = tpitg.y/4; // 0 or 1
const int iy = tpitg.y - 4*ix; // 0...3
@ -351,47 +339,32 @@ kernel void kernel_mul_mat_q4_0_f32(
for (int j = 0; j < 4; ++j) {
acc[0] += yl[j+ 0] * ((int8_t)(xl[j] & 0xF) - m8);
acc[1] += yl[j+16] * ((int8_t)(xl[j] >> 4) - m8);
acc[0] += yl[j] * (xl[j] & 0xF) + yl[j+16] * (xl[j] >> 4);
acc[1] += yl[j] + yl[j+16];
}
sumf += d * (acc[0] + acc[1]);
sumf += d * (acc[0] - 8.f*acc[1]);
}
sum[ith] = sumf;
//
// Accumulate the sum from all threads in the threadgroup
// This version is slightly faster than the commented out one below,
// which I copy-pasted from ggerganov's q4_0 dot product for metal.
//
threadgroup_barrier(mem_flags::mem_threadgroup);
if (ith%4 == 0) {
for (int i = 1; i < 4; ++i) sum[ith] += sum[ith + i];
sum[ith] += sum[ith+1] + sum[ith+2] + sum[ith+3];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (ith%16 == 0) {
for (int i = 4; i < 16; i += 4) sum[ith] += sum[ith + i];
sum[ith] += sum[ith+4] + sum[ith+8] + sum[ith+12];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (ith == 0) {
for (int i = 16; i < nth; i += 16) sum[0] += sum[i];
for (uint i = 16; i < nth; i += 16) sum[0] += sum[i];
dst[r1*ne0 + r0] = sum[0];
}
//// accumulate the sum from all threads in the threadgroup
//threadgroup_barrier(mem_flags::mem_threadgroup);
//for (uint i = nth/2; i > 0; i /= 2) {
// if (ith < i) {
// sum[ith] += sum[ith + i];
// }
// threadgroup_barrier(mem_flags::mem_threadgroup);
//}
//if (ith == 0) {
// dst[r1*ne0 + r0] = sum[0];
//}
}
kernel void kernel_mul_mat_q4_1_f32(
@ -399,20 +372,10 @@ kernel void kernel_mul_mat_q4_1_f32(
device const float * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant int64_t & ne11,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
threadgroup float * sum [[threadgroup(0)]],
uint2 tgpig[[threadgroup_position_in_grid]],
uint2 tpig[[thread_position_in_grid]],
uint2 tpitg[[thread_position_in_threadgroup]],
uint2 tptg[[threads_per_threadgroup]]) {
const int nb = ne00/QK4_1;
@ -460,11 +423,11 @@ kernel void kernel_mul_mat_q4_1_f32(
//
threadgroup_barrier(mem_flags::mem_threadgroup);
if (ith%4 == 0) {
for (int i = 1; i < 4; ++i) sum[ith] += sum[ith + i];
sum[ith] += sum[ith+1] + sum[ith+2] + sum[ith+3];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (ith%16 == 0) {
for (int i = 4; i < 16; i += 4) sum[ith] += sum[ith + i];
sum[ith] += sum[ith+4] + sum[ith+8] + sum[ith+12];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (ith == 0) {
@ -671,6 +634,15 @@ typedef struct {
half d; // super-block scale for quantized scales
half dmin; // super-block scale for quantized mins
} block_q2_k;
// 84 bytes / block
typedef struct {
uint8_t hmask[QK_K/8]; // quants - high bit
uint8_t qs[QK_K/4]; // quants - low 2 bits
uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits
half d; // super-block scale
} block_q3_k;
// 110 bytes / block
typedef struct {
half d; // super-block scale for quantized scales
@ -678,6 +650,16 @@ typedef struct {
uint8_t scales[3*QK_K/64]; // scales and mins, quantized with 6 bits
uint8_t qs[QK_K/2]; // 4--bit quants
} block_q4_k;
// 144 bytes / block
typedef struct {
half d; // super-block scale for quantized scales
half dmin; // super-block scale for quantized mins
uint8_t scales[3*QK_K/64]; // scales and mins, quantized with 6 bits
uint8_t qh[QK_K/8]; // quants, high bit
uint8_t qs[QK_K/2]; // quants, low 4 bits
} block_q5_k;
// 176 bytes / block
typedef struct {
uint8_t ql[QK_K/2]; // quants, lower 4 bits
@ -685,16 +667,19 @@ typedef struct {
int8_t scales[QK_K/16]; // scales, quantized with 8 bits
half d; // super-block scale
} block_q6_k;
// 210 bytes / block
static inline uchar4 get_scale_min_k4(int j, device const uint8_t * q) {
uchar4 r;
if (j < 4) {
r[0] = q[j+0] & 63; r[1] = q[j+4] & 63;
r[2] = q[j+1] & 63; r[3] = q[j+5] & 63;
r[0] = q[j+0] & 63;
r[2] = q[j+1] & 63;
r[1] = q[j+4] & 63;
r[3] = q[j+5] & 63;
} else {
r[0] = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
r[1] = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4);
r[2] = (q[j+5] & 0xF) | ((q[j-3] >> 6) << 4);
r[1] = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4);
r[3] = (q[j+5] >> 4) | ((q[j+1] >> 6) << 4);
}
return r;
@ -735,10 +720,65 @@ static void dequantize_row_q2_k(device const block_q2_k * x, device float * y, i
}
}
static void dequantize_row_q3_k(device const block_q3_k * x, device float * y, int k) {
assert(k % QK_K == 0);
const int nb = k / QK_K;
const uint16_t kmask1 = 0x0303;
const uint16_t kmask2 = 0x0f0f;
uint16_t aux[8];
thread const int8_t * scales = (thread const int8_t*)aux;
for (int i = 0; i < nb; i++) {
const float d_all = (float)(x[i].d);
device const uint8_t * q = x[i].qs;
device const uint8_t * h = x[i].hmask;
uint8_t m = 1;
device const uint16_t * a = (device const uint16_t *)x[i].scales;
aux[0] = (a[0] & kmask2) | (((a[4] >> 0) & kmask1) << 4);
aux[1] = (a[1] & kmask2) | (((a[5] >> 0) & kmask1) << 4);
aux[2] = (a[2] & kmask2) | (((a[4] >> 2) & kmask1) << 4);
aux[3] = (a[3] & kmask2) | (((a[5] >> 2) & kmask1) << 4);
aux[4] = ((a[0] >> 4) & kmask2) | (((a[4] >> 4) & kmask1) << 4);
aux[5] = ((a[1] >> 4) & kmask2) | (((a[5] >> 4) & kmask1) << 4);
aux[6] = ((a[2] >> 4) & kmask2) | (((a[4] >> 6) & kmask1) << 4);
aux[7] = ((a[3] >> 4) & kmask2) | (((a[5] >> 6) & kmask1) << 4);
int is = 0;
float dl;
for (int n = 0; n < QK_K; n += 128) {
int shift = 0;
for (int j = 0; j < 4; ++j) {
dl = d_all * (scales[is++] - 32);
for (int l = 0; l < 16; ++l) {
*y++ = dl * ((int8_t)((q[l+ 0] >> shift) & 3) - ((h[l+ 0] & m) ? 0 : 4));
}
dl = d_all * (scales[is++] - 32);
for (int l = 0; l < 16; ++l) {
*y++ = dl * ((int8_t)((q[l+16] >> shift) & 3) - ((h[l+16] & m) ? 0 : 4));
}
shift += 2;
m <<= 1;
}
q += 32;
}
}
}
static void dequantize_row_q4_k(device const block_q4_k * x, device float * y, int k) {
assert(k % QK_K == 0);
const int nb = k / QK_K;
for (int i = 0; i < nb; i++) {
const float d = x[i].d;
@ -760,6 +800,33 @@ static void dequantize_row_q4_k(device const block_q4_k * x, device float * y, i
}
}
static void dequantize_row_q5_k(device const block_q5_k * x, device float * y, int k) {
assert(k % QK_K == 0);
const int nb = k / QK_K;
for (int i = 0; i < nb; i++) {
const float d = (float)(x[i].d);
const float min = (float)(x[i].dmin);
device const uint8_t * ql = x[i].qs;
device const uint8_t * qh = x[i].qh;
int is = 0;
uint8_t u1 = 1, u2 = 2;
for (int j = 0; j < QK_K; j += 64) {
const uchar4 sc = get_scale_min_k4(is, x[i].scales);
const float d1 = d * sc[0]; const float m1 = min * sc[1];
const float d2 = d * sc[2]; const float m2 = min * sc[3];
for (int l = 0; l < 32; ++l) *y++ = d1 * ((ql[l] & 0xF) + (qh[l] & u1 ? 16 : 0)) - m1;
for (int l = 0; l < 32; ++l) *y++ = d2 * ((ql[l] >> 4) + (qh[l] & u2 ? 16 : 0)) - m2;
ql += 32; is += 2;
u1 <<= 2; u2 <<= 2;
}
}
}
static void dequantize_row_q6_k(device const block_q6_k * x, device float * y, int k) {
assert(k % QK_K == 0);
const int nb = k / QK_K;
@ -808,6 +875,22 @@ kernel void kernel_get_rows_q2_k(
(device float *) ((device char *) dst + i*nb1), ne00);
}
kernel void kernel_get_rows_q3_k(
device const void * src0,
device const int * src1,
device float * dst,
constant int64_t & ne00,
constant uint64_t & nb01,
constant uint64_t & nb1,
uint tpig[[thread_position_in_grid]]) {
const int i = tpig;
const int r = ((device int32_t *) src1)[i];
dequantize_row_q3_k(
(device const block_q3_k *) ((device char *) src0 + r*nb01),
(device float *) ((device char *) dst + i*nb1), ne00);
}
kernel void kernel_get_rows_q4_k(
device const void * src0,
device const int * src1,
@ -824,6 +907,22 @@ kernel void kernel_get_rows_q4_k(
(device float *) ((device char *) dst + i*nb1), ne00);
}
kernel void kernel_get_rows_q5_k(
device const void * src0,
device const int * src1,
device float * dst,
constant int64_t & ne00,
constant uint64_t & nb01,
constant uint64_t & nb1,
uint tpig[[thread_position_in_grid]]) {
const int i = tpig;
const int r = ((device int32_t *) src1)[i];
dequantize_row_q5_k(
(device const block_q5_k *) ((device char *) src0 + r*nb01),
(device float *) ((device char *) dst + i*nb1), ne00);
}
kernel void kernel_get_rows_q6_k(
device const void * src0,
device const int * src1,
@ -847,20 +946,10 @@ kernel void kernel_mul_mat_q2_k_f32(
device const float * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant int64_t & ne11,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
threadgroup float * sum [[threadgroup(0)]],
uint2 tgpig[[threadgroup_position_in_grid]],
uint2 tpig[[thread_position_in_grid]], // we don't use this for now
uint2 tpitg[[thread_position_in_threadgroup]],
uint2 tptg[[threads_per_threadgroup]]) {
@ -875,7 +964,6 @@ kernel void kernel_mul_mat_q2_k_f32(
const int nth = tptg.x*tptg.y;
const int ith = tptg.y*tpitg.x + tpitg.y;
const int tid = tpitg.y; // 0...16
const int il = tid/4; // 0...3
const int ir = tid%4; // 0...3
@ -885,35 +973,54 @@ kernel void kernel_mul_mat_q2_k_f32(
const int n = 8;
const int is = 4*il + (n*ir)/16;
const int y_offset = 64*il + n*ir;
const int q_offset = 32*ip + n*ir;
sum[ith] = 0.0f;
float sumf = 0;
for (int i = tpitg.x; i < nb; i += tptg.x) {
device const uint8_t * q = x[i].qs + 32*ip + n*ir;
device const uint8_t * q = x[i].qs + q_offset;
device const uint8_t * scales = x[i].scales + is;
uint8_t d1 = scales[0] & 0xF;
uint8_t m1 = scales[0] >> 4;
uint8_t d2 = scales[2] & 0xF;
uint8_t m1 = scales[0] >> 4;
uint8_t m2 = scales[2] >> 4;
device const float * y = yy + i*QK_K + 64*il + n*ir;
device const float * y = yy + i*QK_K + y_offset;
//float4 s = {0.f, 0.f, 0.f, 0.f};
float2 s = {0.f, 0.f};
float smin = 0;
for (int l = 0; l < n; ++l) {
s[0] += y[l+ 0] * ((q[l] >> shift1) & 3);
s[1] += y[l+32] * ((q[l] >> shift2) & 3);
smin += y[l+ 0] * m1 + y[l+32] * m2;
}
const float dall = (float)x[i].d;
const float dmin = (float)x[i].dmin;
float4 s = {0.f, 0.f, 0.f, 0.f};
for (int l = 0; l < n; ++l) {
s[0] += y[l+ 0] * ((q[l] >> shift1) & 3); s[1] += y[l+ 0];
s[2] += y[l+32] * ((q[l] >> shift2) & 3); s[3] += y[l+32];
}
sumf += dall * (s[0] * d1 + s[2] * d2) - dmin * (s[1] * m1 + s[3] * m2);
sumf += dall * (s[0] * d1 + s[1] * d2) - dmin * smin;
}
sum[ith] = sumf;
//int mask1 = (ith%4 == 0);
//int mask2 = (ith%16 == 0);
//threadgroup_barrier(mem_flags::mem_threadgroup);
//for (int i = 1; i < 4; ++i) sum[ith] += mask1 * sum[ith + i];
//threadgroup_barrier(mem_flags::mem_threadgroup);
//for (int i = 4; i < 16; i += 4) sum[ith] += mask2 * sum[ith + i];
//threadgroup_barrier(mem_flags::mem_threadgroup);
//if (ith == 0) {
// for (int i = 16; i < nth; i += 16) sum[0] += sum[i];
// dst[r1*ne0 + r0] = sum[0];
//}
//
// Accumulate the sum from all threads in the threadgroup
// This version is slightly faster than the commented out one below,
@ -932,19 +1039,109 @@ kernel void kernel_mul_mat_q2_k_f32(
for (int i = 16; i < nth; i += 16) sum[0] += sum[i];
dst[r1*ne0 + r0] = sum[0];
}
}
//// accumulate the sum from all threads in the threadgroup
//threadgroup_barrier(mem_flags::mem_threadgroup);
//for (uint i = nth/2; i > 0; i /= 2) {
// if (ith < i) {
// sum[ith] += sum[ith + i];
// }
// threadgroup_barrier(mem_flags::mem_threadgroup);
//}
kernel void kernel_mul_mat_q3_k_f32(
device const void * src0,
device const float * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne10,
constant int64_t & ne0,
constant int64_t & ne1,
threadgroup float * sum [[threadgroup(0)]],
uint2 tgpig[[threadgroup_position_in_grid]],
uint2 tpitg[[thread_position_in_threadgroup]],
uint2 tptg[[threads_per_threadgroup]]) {
const uint16_t kmask1 = 0x0303;
const uint16_t kmask2 = 0x0f0f;
const uint8_t m3 = 3;
const int8_t m4 = 4;
const int nb = ne00/QK_K;
const int64_t r0 = tgpig.x;
const int64_t r1 = tgpig.y;
device const block_q3_k * x = (device const block_q3_k *) src0 + r0*nb;
device const float * yy = (device const float *) src1 + r1*ne10;
const int nth = tptg.x*tptg.y;
const int ith = tptg.y*tpitg.x + tpitg.y;
const int tid = tpitg.y; // expecting 16
const int ip = tid/8; // 0 or 1
const int il = tid/2 - 4*ip; // 0...3
const int ir = tid%2;
const int n = 8;
const int l0 = n*ir;
const uint8_t m = 1 << (4*ip + il);
const int shift = 2*il;
const uint16_t s_shift1 = 4*ip;
const uint16_t s_shift2 = s_shift1 + 2*(il/2);
const int ik = 4 + (il%2);
const int q_offset = 32*ip + l0;
const int y_offset = 128*ip + 32*il + l0;
//float sumf = 0;
float sumf1 = 0, sumf2 = 0;
for (int i = tpitg.x; i < nb; i += tptg.x) {
const float d_all = (float)(x[i].d);
device const uint8_t * q = x[i].qs + q_offset;
device const uint8_t * h = x[i].hmask + l0;
device const float * y = yy + i * QK_K + y_offset;
device const uint16_t * a = (device const uint16_t *)x[i].scales;
const char2 scales = as_type<char2>((uint16_t)(((a[il] >> s_shift1) & kmask2) | (((a[ik] >> s_shift2) & kmask1) << 4)));
float s = 0;
for (int l = 0; l < n; ++l) {
s += y[l+ 0] * ((int8_t)((q[l+ 0] >> shift) & m3) - ((h[l+ 0] & m) ? 0 : m4));
}
float d = d_all * s;
sumf1 += d * scales[0];
sumf2 += d;
//sumf += d_all * s * (scales[0] - 32);
s = 0;
for (int l = 0; l < n; ++l) {
s += y[l+16] * ((int8_t)((q[l+16] >> shift) & m3) - ((h[l+16] & m) ? 0 : m4));
}
d = d_all * s;
sumf1 += d * scales[1];
sumf2 += d;
//sumf += d_all * s * (scales[1] - 32);
}
//sum[ith] = sumf;
sum[ith] = sumf1 - 32.f*sumf2;
//
// Accumulate the sum from all threads in the threadgroup
//
threadgroup_barrier(mem_flags::mem_threadgroup);
if (ith%4 == 0) {
for (int i = 1; i < 4; ++i) sum[ith] += sum[ith + i];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (ith%16 == 0) {
for (int i = 4; i < 16; i += 4) sum[ith] += sum[ith + i];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (ith == 0) {
for (int i = 16; i < nth; i += 16) sum[0] += sum[i];
dst[r1*ne0 + r0] = sum[0];
}
//if (ith == 0) {
// dst[r1*ne0 + r0] = sum[0];
//}
}
kernel void kernel_mul_mat_q4_k_f32(
@ -952,23 +1149,17 @@ kernel void kernel_mul_mat_q4_k_f32(
device const float * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant int64_t & ne11,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
threadgroup float * sum [[threadgroup(0)]],
uint2 tgpig[[threadgroup_position_in_grid]],
uint2 tpig[[thread_position_in_grid]], // we don't use this for now
uint2 tpitg[[thread_position_in_threadgroup]],
uint2 tptg[[threads_per_threadgroup]]) {
const uint16_t kmask1 = 0x3f3f;
const uint16_t kmask2 = 0x0f0f;
const uint16_t kmask3 = 0xc0c0;
const int nb = ne00/QK_K;
const int64_t r0 = tgpig.x;
@ -977,37 +1168,55 @@ kernel void kernel_mul_mat_q4_k_f32(
device const block_q4_k * x = (device const block_q4_k *) src0 + r0*nb;
device const float * yy = (device const float *) src1 + r1*ne10;
const uint nth = tptg.x*tptg.y;
const uint ith = tptg.y*tpitg.x + tpitg.y;
const int nth = tptg.x*tptg.y;
const int ith = tptg.y*tpitg.x + tpitg.y;
const int tid = tpitg.y; // 0...16
const int il = tid/4; // 0...3
const int ir = tid%4; // 0...3
const int n = 8;
const int is = 2*il;
const int ir = tid - 4*il;// 0...3
const int n = 4;
const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
const int in = il%2;
const int l0 = n*(2*ir + in);
const int q_offset = 32*im + l0;
const int y_offset = 64*im + l0;
sum[ith] = 0.0f;
uchar2 sc1, sc2, sc3, sc4;
float sumf = 0;
for (int i = tpitg.x; i < nb; i += tptg.x) {
device const uint8_t * q = (x + i)->qs + 32*il + n*ir;
device const float * y = yy + i*QK_K + 64*il + n*ir;
device const uint8_t * scales = (x + i)->scales;
device const uint8_t * q1 = (x + i)->qs + q_offset;
device const uint8_t * q2 = q1 + 64;
device const float * y1 = yy + i*QK_K + y_offset;
device const float * y2 = y1 + 128;
const float dall = (float)((x + i)->d);
const float dmin = (float)((x + i)->dmin);
const uchar4 sc = get_scale_min_k4(is, scales);
device const uint16_t * a = (device const uint16_t *)(x + i)->scales;
sc1 = as_type<uchar2>((uint16_t)(a[im+0] & kmask1));
sc2 = as_type<uchar2>((uint16_t)(a[im+2] & kmask1));
sc3 = as_type<uchar2>((uint16_t)(((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2)));
sc4 = as_type<uchar2>((uint16_t)(((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2)));
float4 s = {0.f, 0.f, 0.f, 0.f};
float smin = 0;
for (int l = 0; l < n; ++l) {
s[0] += y[l+ 0] * (q[l] & 0xF); s[1] += y[l+ 0];
s[2] += y[l+32] * (q[l] >> 4); s[3] += y[l+32];
s[0] += y1[l] * (q1[l] & 0xF); s[1] += y1[l+32] * (q1[l] >> 4);
s[2] += y2[l] * (q2[l] & 0xF); s[3] += y2[l+32] * (q2[l] >> 4);
smin += y1[l] * sc2[0] + y1[l+32] * sc2[1] + y2[l] * sc4[0] + y2[l+32] * sc4[1];
}
sumf += dall * (s[0] * sc[0] + s[2] * sc[2]) - dmin * (s[1] * sc[1] + s[3] * sc[3]);
sumf += dall * (s[0] * sc1[0] + s[1] * sc1[1] + s[2] * sc3[0] + s[3] * sc3[1]) - dmin * smin;
}
sum[ith] = sumf;
//
@ -1043,25 +1252,114 @@ kernel void kernel_mul_mat_q4_k_f32(
//}
}
kernel void kernel_mul_mat_q5_k_f32(
device const void * src0,
device const float * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne10,
constant int64_t & ne0,
threadgroup float * sum [[threadgroup(0)]],
uint2 tgpig[[threadgroup_position_in_grid]],
uint2 tpitg[[thread_position_in_threadgroup]],
uint2 tptg[[threads_per_threadgroup]]) {
const uint16_t kmask1 = 0x3f3f;
const uint16_t kmask2 = 0x0f0f;
const uint16_t kmask3 = 0xc0c0;
const int nb = ne00/QK_K;
const int64_t r0 = tgpig.x;
const int64_t r1 = tgpig.y;
device const block_q5_k * x = (device const block_q5_k *) src0 + r0*nb;
device const float * yy = (device const float *) src1 + r1*ne10;
const int nth = tptg.x*tptg.y;
const int ith = tptg.y*tpitg.x + tpitg.y;
const int tid = tpitg.y; // 0...16
const int il = tid/4; // 0...3
const int ir = tid - 4*il;// 0...3
const int n = 4;
const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
const int in = il%2;
const int l0 = n*(2*ir + in);
const int q_offset = 32*im + l0;
const int y_offset = 64*im + l0;
const uint8_t hm1 = 1u << (2*im);
const uint8_t hm2 = hm1 << 1;
const uint8_t hm3 = hm1 << 4;
const uint8_t hm4 = hm2 << 4;
uchar2 sc1, sc2, sc3, sc4;
float sumf = 0;
for (int i = tpitg.x; i < nb; i += tptg.x) {
device const uint8_t * q1 = (x + i)->qs + q_offset;
device const uint8_t * q2 = q1 + 64;
device const uint8_t * qh = (x + i)->qh + l0;
device const float * y1 = yy + i*QK_K + y_offset;
device const float * y2 = y1 + 128;
const float dall = (float)((x + i)->d);
const float dmin = (float)((x + i)->dmin);
device const uint16_t * a = (device const uint16_t *)(x + i)->scales;
sc1 = as_type<uchar2>((uint16_t)(a[im+0] & kmask1));
sc2 = as_type<uchar2>((uint16_t)(a[im+2] & kmask1));
sc3 = as_type<uchar2>((uint16_t)(((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2)));
sc4 = as_type<uchar2>((uint16_t)(((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2)));
float4 s = {0.f, 0.f, 0.f, 0.f};
float smin = 0;
for (int l = 0; l < n; ++l) {
s[0] += y1[l+ 0] * ((q1[l] & 0xF) + (qh[l] & hm1 ? 16 : 0));
s[1] += y1[l+32] * ((q1[l] >> 4) + (qh[l] & hm2 ? 16 : 0));
s[2] += y2[l+ 0] * ((q2[l] & 0xF) + (qh[l] & hm3 ? 16 : 0));
s[3] += y2[l+32] * ((q2[l] >> 4) + (qh[l] & hm4 ? 16 : 0));
smin += y1[l] * sc2[0] + y1[l+32] * sc2[1] + y2[l] * sc4[0] + y2[l+32] * sc4[1];
}
sumf += dall * (s[0] * sc1[0] + s[1] * sc1[1] + s[2] * sc3[0] + s[3] * sc3[1]) - dmin * smin;
}
sum[ith] = sumf;
//
// Accumulate the sum from all threads in the threadgroup
//
threadgroup_barrier(mem_flags::mem_threadgroup);
if (ith%4 == 0) {
sum[ith] += sum[ith+1] + sum[ith+2] + sum[ith+3];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (ith%16 == 0) {
sum[ith] += sum[ith+4] + sum[ith+8] + sum[ith+12];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
if (ith == 0) {
for (int i = 16; i < nth; i += 16) sum[0] += sum[i];
dst[r1*ne0 + r0] = sum[0];
}
}
kernel void kernel_mul_mat_q6_k_f32(
device const void * src0,
device const float * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant int64_t & ne11,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
threadgroup float * sum [[threadgroup(0)]],
uint2 tgpig[[threadgroup_position_in_grid]],
uint2 tpig[[thread_position_in_grid]], // we don't use this for now
uint2 tpitg[[thread_position_in_threadgroup]],
uint2 tptg[[threads_per_threadgroup]]) {
@ -1078,24 +1376,29 @@ kernel void kernel_mul_mat_q6_k_f32(
device const block_q6_k * x = (device const block_q6_k *) src0 + r0*nb;
device const float * yy = (device const float *) src1 + r1*ne10;
const uint nth = tptg.x*tptg.y;
const uint ith = tptg.y*tpitg.x + tpitg.y;
const int nth = tptg.x*tptg.y;
const int ith = tptg.y*tpitg.x + tpitg.y;
const int step = QK_K / tptg.y; // we expect this to be 16
const int iqs = step * tpitg.y; // 0...240 in steps of 16
// Note: we absolutely assume that tptg.y = 16 and QK_K = 256!
const int iqs = 16 * tpitg.y;
const int ip = iqs / 128; // 0 or 1
const int il = (iqs - 128*ip)/16; // 0...7
const int n = 4;
const int is = 8*ip + (n*il)/16;
const int l0 = n*il;
const int is = 8*ip + l0/16;
const int y_offset = 128*ip + l0;
const int q_offset_l = 64*ip + l0;
const int q_offset_h = 32*ip + l0;
float sumf = 0;
for (int i = tpitg.x; i < nb; i += tptg.x) {
device const uint8_t * ql = x[i].ql + 64*ip + n*il;
device const uint8_t * qh = x[i].qh + 32*ip + n*il;
device const uint8_t * ql = x[i].ql + q_offset_l;
device const uint8_t * qh = x[i].qh + q_offset_h;
device const int8_t * sc = x[i].scales + is;
device const float * y = yy + i * QK_K + 128*ip + n*il;
device const float * y = yy + i * QK_K + y_offset;
const float dall = x[i].d;

35
ggml-opencl.cpp
View file

@ -1167,7 +1167,7 @@ size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct g
return 0;
}
void ggml_cl_transform_tensor(ggml_tensor * tensor) {
void ggml_cl_transform_tensor(void * data, ggml_tensor * tensor) {
const int64_t ne0 = tensor->ne[0];
const int64_t ne1 = tensor->ne[1];
const int64_t ne2 = tensor->ne[2];
@ -1179,6 +1179,7 @@ void ggml_cl_transform_tensor(ggml_tensor * tensor) {
size_t q_size;
cl_mem dst = ggml_cl_pool_malloc(q_sz, &q_size);
tensor->data = data;
// copy tensor to device
for (int64_t i3 = 0; i3 < ne3; i3++) {
for (int64_t i2 = 0; i2 < ne2; i2++) {
@ -1190,35 +1191,5 @@ void ggml_cl_transform_tensor(ggml_tensor * tensor) {
CL_CHECK(clFinish(queue));
tensor->data = dst;
tensor->backend = GGML_BACKEND_GPU;
}
void ggml_cl_load_data(const char * fname, struct ggml_tensor * tensor, const size_t offset) {
cl_int err;
FILE * fp = fopen(fname, "rb");
const size_t size = ggml_nbytes(tensor);
cl_mem dst;
CL_CHECK((dst = clCreateBuffer(context, CL_MEM_READ_ONLY, size, nullptr, &err), err));
void * buf_host = malloc(size);
#ifdef _WIN32
int ret = _fseeki64(fp, (__int64) offset, SEEK_SET);
#else
int ret = fseek(fp, (long) offset, SEEK_SET);
#endif
GGML_ASSERT(ret == 0); // same
size_t ret2 = fread(buf_host, size, 1, fp);
if (ret2 != 1) {
fprintf(stderr, "unexpectedly reached end of file");
exit(1);
}
clEnqueueWriteBuffer(queue, dst, CL_TRUE, 0, size, buf_host, 0, nullptr, nullptr);
tensor->data = dst;
free(buf_host);
fclose(fp);
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
}

3
ggml-opencl.h
View file

@ -18,8 +18,7 @@ void ggml_cl_host_free(void * ptr);
void ggml_cl_free_data(const struct ggml_tensor* tensor);
void ggml_cl_transform_tensor(struct ggml_tensor * tensor);
void ggml_cl_load_data(const char * fname, struct ggml_tensor * tensor, size_t offset);
void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor);
#ifdef __cplusplus
}

6
k_quants.c
View file

@ -1519,7 +1519,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
const uint8x16_t m4b = vdupq_n_u8(0xf);
#ifdef __ARM_FEATURE_DOTPROD
const uint32x4_t mzero = vdupq_n_s32(0);
const int32x4_t mzero = vdupq_n_s32(0);
#endif
int8x16x2_t q4bytes;
@ -1745,7 +1745,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
#ifdef __ARM_NEON
const uint8x16_t m4b = vdupq_n_u8(0xf);
const uint32x4_t mzero = vdupq_n_u32(0);
const int32x4_t mzero = vdupq_n_s32(0);
const uint8x16_t mone = vdupq_n_u8(1);
const uint8x16_t mtwo = vdupq_n_u8(2);
@ -2242,5 +2242,3 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
*s = sumf;
#endif
}

117
llama.cpp
View file

@ -707,6 +707,9 @@ struct llama_model_loader {
struct ggml_tensor * get_tensor_for(llama_load_tensor & lt, ggml_backend backend) {
struct ggml_tensor * tensor;
if (backend != GGML_BACKEND_CPU) {
ggml_set_no_alloc(ggml_ctx, true);
}
if (lt.ne.size() == 2) {
tensor = ggml_new_tensor_2d(ggml_ctx, lt.type, lt.ne.at(0), lt.ne.at(1));
} else {
@ -716,6 +719,9 @@ struct llama_model_loader {
ggml_set_name(tensor, lt.name.c_str());
LLAMA_ASSERT(lt.ggml_tensor == NULL); // if this fails, we called get_tensor twice on the same tensor
if (backend != GGML_BACKEND_CPU) {
ggml_set_no_alloc(ggml_ctx, use_mmap);
}
tensor->backend = backend;
lt.ggml_tensor = tensor;
num_ggml_tensors_created++;
@ -731,6 +737,7 @@ struct llama_model_loader {
void load_all_data(llama_progress_callback progress_callback, void * progress_callback_user_data, llama_mlock * lmlock) {
size_t data_size = 0;
size_t prefetch_size = 0;
size_t lock_size = 0;
for (const llama_load_tensor & lt : tensors_map.tensors) {
data_size += lt.size;
if (lt.ggml_tensor->backend == GGML_BACKEND_CPU) {
@ -740,11 +747,6 @@ struct llama_model_loader {
if (use_mmap) {
mapping.reset(new llama_mmap(&file_loaders.at(0)->file, prefetch_size));
if (!lmlock) {
// Don't call the callback since the actual loading will be lazy
// and we can't measure it.
progress_callback = NULL;
}
if (lmlock) {
lmlock->init(mapping->addr);
}
@ -752,20 +754,49 @@ struct llama_model_loader {
size_t done_size = 0;
for (llama_load_tensor & lt : tensors_map.tensors) {
if (lt.ggml_tensor->backend != GGML_BACKEND_CPU) {
continue;
}
if (progress_callback) {
progress_callback((float) done_size / data_size, progress_callback_user_data);
}
LLAMA_ASSERT(lt.ggml_tensor); // unused tensors should have been caught by load_data already
lt.data = (uint8_t *) lt.ggml_tensor->data;
load_data_for(lt);
lt.ggml_tensor->data = lt.data;
done_size += lt.size;
if (use_mmap && lmlock) {
lmlock->grow_to(done_size);
// allocate temp buffer if not using mmap
if (!use_mmap && lt.data == NULL) {
GGML_ASSERT(lt.ggml_tensor->backend != GGML_BACKEND_CPU);
lt.data = (uint8_t*)malloc(ggml_nbytes(lt.ggml_tensor));
}
load_data_for(lt);
switch(lt.ggml_tensor->backend) {
case GGML_BACKEND_CPU:
lt.ggml_tensor->data = lt.data;
if (use_mmap && lmlock) {
lock_size += lt.size;
lmlock->grow_to(lock_size);
}
break;
#if defined(GGML_USE_CUBLAS)
case GGML_BACKEND_GPU:
case GGML_BACKEND_GPU_SPLIT:
ggml_cuda_transform_tensor(lt.data, lt.ggml_tensor);
if (!use_mmap) {
free(lt.data);
}
break;
#elif defined(GGML_USE_CLBLAST)
case GGML_BACKEND_GPU:
ggml_cl_transform_tensor(lt.data, lt.ggml_tensor);
if (!use_mmap) {
free(lt.data);
}
break;
#endif
default:
continue;
}
done_size += lt.size;
}
}
@ -1141,7 +1172,7 @@ static void llama_model_load_internal(
if (backend == GGML_BACKEND_GPU) {
vram_weights +=
ggml_nbytes(layer.attention_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.attention_norm) +
ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) +
ggml_nbytes(layer.w1) + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
}
}
@ -1196,58 +1227,14 @@ static void llama_model_load_internal(
model.tensors_by_name.emplace_back(lt.name, lt.ggml_tensor);
}
ml->load_all_data(progress_callback, progress_callback_user_data, use_mlock ? &lctx.model.mlock_mmap : NULL);
#if defined(GGML_USE_CUBLAS)
{
ggml_cuda_set_tensor_split(tensor_split);
size_t done_size = 0;
size_t data_size = 0;
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
data_size += lt.size;
if (lt.ggml_tensor->backend == GGML_BACKEND_CPU) {
done_size += lt.size;
}
}
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
ggml_backend backend = lt.ggml_tensor->backend;
if (backend != GGML_BACKEND_GPU && backend != GGML_BACKEND_GPU_SPLIT) {
continue;
}
if (progress_callback) {
progress_callback((float) done_size / data_size, progress_callback_user_data);
}
ggml_cuda_load_data(fname.c_str(), lt.ggml_tensor, lt.shards.at(0).file_off);
done_size += lt.size;
}
}
#elif defined(GGML_USE_CLBLAST)
{
size_t done_size = 0;
size_t data_size = 0;
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
data_size += lt.size;
if (lt.ggml_tensor->backend == GGML_BACKEND_CPU) {
done_size += lt.size;
}
}
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
if (lt.ggml_tensor->backend != GGML_BACKEND_GPU) {
continue;
}
if (progress_callback) {
progress_callback((float) done_size / data_size, progress_callback_user_data);
}
ggml_cl_load_data(fname.c_str(), lt.ggml_tensor, lt.shards.at(0).file_off);
done_size += lt.size;
}
}
#else
(void) n_batch;
(void) tensor_split;
#endif
ml->load_all_data(progress_callback, progress_callback_user_data, use_mlock ? &lctx.model.mlock_mmap : NULL);
if (progress_callback) {
progress_callback(1.0f, progress_callback_user_data);
}
@ -2390,12 +2377,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
printf("size = %8.3f MB\n", tensor.size/1024.0/1024.0);
} else {
new_type = quantized_type;
// TODO: temporary disabled until Metal / OpenCL support is available
// ref: https://github.com/ggerganov/llama.cpp/issues/1711
//if (tensor.name == "output.weight") {
// new_type = GGML_TYPE_Q6_K;
//}
if (tensor.name.find("attention.wv.weight") != std::string::npos) {
if (tensor.name == "output.weight") {
new_type = GGML_TYPE_Q6_K;
}
else if (tensor.name.find("attention.wv.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&