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@SlyEcho
SlyEcho committed Jun 17, 2023
2 parents 6f7c156 + b241649 commit 67e229b
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Showing 11 changed files with 264 additions and 50 deletions.
1 change: 1 addition & 0 deletions .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -34,6 +34,7 @@ models/*
/perplexity
/embedding
/train-text-from-scratch
/simple
/benchmark-matmult
/vdot
/server
Expand Down
9 changes: 1 addition & 8 deletions Makefile
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Original file line number Diff line number Diff line change
Expand Up @@ -144,11 +144,7 @@ endif # LLAMA_NO_ACCELERATE

ifdef LLAMA_OPENBLAS
CFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/openblas -I/usr/include/openblas
ifneq ($(shell grep -e "Arch Linux" -e "ID_LIKE=arch" /etc/os-release 2>/dev/null),)
LDFLAGS += -lopenblas -lcblas
else
LDFLAGS += -lopenblas
endif
LDFLAGS += -lopenblas
endif # LLAMA_OPENBLAS

ifdef LLAMA_BLIS
Expand Down Expand Up @@ -298,9 +294,6 @@ main: examples/main/main.cpp build-info.h ggml.

simple: examples/simple/simple.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
@echo
@echo '==== Run ./simple -h for help. ===='
@echo

quantize: examples/quantize/quantize.cpp build-info.h ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
Expand Down
1 change: 0 additions & 1 deletion README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -336,7 +336,6 @@ Building the program with BLAS support may lead to some performance improvements
cmake .. -DLLAMA_CUBLAS=ON
cmake --build . --config Release
```
Note: Because llama.cpp uses multiple CUDA streams for matrix multiplication results [are not guaranteed to be reproducible](https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility). If you need reproducibility, set `GGML_CUDA_MAX_STREAMS` in the file `ggml-cuda.cu` to 1.
The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used.
Expand Down
1 change: 1 addition & 0 deletions examples/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -38,6 +38,7 @@ else()
add_subdirectory(benchmark)
add_subdirectory(baby-llama)
add_subdirectory(train-text-from-scratch)
add_subdirectory(simple)
if (LLAMA_METAL)
add_subdirectory(metal)
endif()
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3 changes: 0 additions & 3 deletions examples/common.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -106,9 +106,6 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
}

if (arg == "-s" || arg == "--seed") {
#if defined(GGML_USE_CUBLAS)
fprintf(stderr, "WARNING: when using cuBLAS generation results are NOT guaranteed to be reproducible.\n");
#endif
if (++i >= argc) {
invalid_param = true;
break;
Expand Down
2 changes: 1 addition & 1 deletion examples/main/main.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -354,7 +354,7 @@ int main(int argc, char ** argv) {
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 %" PRIu64 " token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
printf("<<input too long: skipped %zu token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
fflush(stdout);
embd.resize(max_embd_size);
Expand Down
58 changes: 24 additions & 34 deletions ggml-cuda.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -68,6 +68,10 @@
#include "ggml-cuda.h"
#include "ggml.h"

#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif

static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");

#define CUDA_CHECK(err) \
Expand Down Expand Up @@ -1518,19 +1522,13 @@ static void * g_scratch_buffer = nullptr;
static size_t g_scratch_size = 1024*1024*1024; // 1 GB by default
static size_t g_scratch_offset = 0;

#define GGML_CUDA_MAX_STREAMS 8 // Set this to 1 for reproducible matrix multiplication.
#define GGML_CUDA_MAX_EVENTS 64

static int g_device_count = -1;
static int g_main_device = 0;
static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0};

static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};

static cudaStream_t g_cudaStreams_main[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS] = { nullptr };

static cudaStream_t g_cudaStreams_memcpy_src1[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS] = { nullptr };
static cudaEvent_t g_cudaEvents_memcpy_src1[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_EVENTS] = { nullptr };
static cudaStream_t g_cudaStreams_main[GGML_CUDA_MAX_DEVICES] = { nullptr };

void ggml_init_cublas() {
static bool initialized = false;
Expand All @@ -1554,15 +1552,8 @@ void ggml_init_cublas() {
for (int id = 0; id < g_device_count; ++id) {
CUDA_CHECK(cudaSetDevice(id));

// create streams
for (int i = 0; i < GGML_CUDA_MAX_STREAMS; ++i) {
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams_main[id][i], cudaStreamNonBlocking));
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams_memcpy_src1[id][i], cudaStreamNonBlocking));
}
// create events
for (int i = 0; i < GGML_CUDA_MAX_EVENTS; ++i) {
CUDA_CHECK(cudaEventCreateWithFlags(&g_cudaEvents_memcpy_src1[id][i], cudaEventDisableTiming));
}
// create main stream
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams_main[id], cudaStreamNonBlocking));

// create cublas handle
CUBLAS_CHECK(cublasCreate(&g_cublas_handles[id]));
Expand Down Expand Up @@ -2029,6 +2020,12 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
size_t src1_asf[GGML_CUDA_MAX_DEVICES] = {0};
size_t dst_asf[GGML_CUDA_MAX_DEVICES] = {0};

// if multiple GPUs are used they need to wait for the main GPU to finish
if (split && g_device_count > 1) {
CUDA_CHECK(cudaSetDevice(g_main_device));
CUDA_CHECK(cudaDeviceSynchronize());
}

for (int id = 0; id < g_device_count; ++id) {
if (!split && id != g_main_device) {
continue;
Expand Down Expand Up @@ -2127,9 +2124,7 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
}
const int64_t i11 = i13*ne12 + i12;

cudaStream_t cudaStream_main = g_cudaStreams_main[id][i0 % GGML_CUDA_MAX_STREAMS];
cudaStream_t cudaStream_memcpy_src1 = g_cudaStreams_memcpy_src1[id][i0 % GGML_CUDA_MAX_STREAMS];
cudaEvent_t cudaEvent_memcpy_src1 = g_cudaEvents_memcpy_src1[id][i0 % GGML_CUDA_MAX_EVENTS];
cudaStream_t cudaStream_main = g_cudaStreams_main[id];

// for split tensors the data begins at i0 == i0_offset_low
char * src0_ddq_i = src0_ddq[id] + (i0 - i0_offset_low)*src0_stride*src0_ts/src0_bs;
Expand Down Expand Up @@ -2157,14 +2152,14 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
if (src1->backend == GGML_BACKEND_CPU) {
GGML_ASSERT(!flatten_rows || nrows0 == ggml_nrows(src1));
int64_t nrows1 = flatten_rows ? nrows0 : ne11;
CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src1_ddf_i, src1, i03, i02, 0, nrows1, cudaStream_memcpy_src1));
CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src1_ddf_i, src1, i03, i02, 0, nrows1, cudaStream_main));
} else if (src1->backend == GGML_BACKEND_GPU && src1_is_contiguous) {
if (id != g_main_device) {
GGML_ASSERT(!flatten_rows);
float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device];
src1_ddf_i_source += i11*src1_stride;
CUDA_CHECK(cudaMemcpyAsync(src1_ddf_i, src1_ddf_i_source, src1_stride*sizeof(float),
cudaMemcpyDeviceToDevice, cudaStream_memcpy_src1));
cudaMemcpyDeviceToDevice, cudaStream_main));
}
} else if (src1_on_device && !src1_is_contiguous) {
GGML_ASSERT(!split);
Expand All @@ -2173,7 +2168,6 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
GGML_ASSERT(false);
}
}
CUDA_CHECK(cudaEventRecord(cudaEvent_memcpy_src1, cudaStream_memcpy_src1));

if (!src0_on_device || !src0_is_contiguous) {
if (src0_is_f32) {
Expand All @@ -2189,9 +2183,6 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm
CUDA_CHECK(cudaGetLastError());
}

// wait with main stream until src1 memcpy is done
CUDA_CHECK(cudaStreamWaitEvent(cudaStream_main, cudaEvent_memcpy_src1, 0));

// do the computation
op(src0, src1, dst, src0_ddq_i, src0_ddf_i, src1_ddf_i, dst_ddf_i, i02, i01_low, i01_high, i11, cudaStream_main);

Expand Down Expand Up @@ -2229,8 +2220,13 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm

// wait until each device is finished, then free their buffers
for (int id = 0; id < g_device_count; ++id) {
if (src0_asq[id] == 0 && src0_asf[id] == 0 && src1_asf[id] == 0 && dst_asf[id] == 0) {
continue;
}

CUDA_CHECK(cudaSetDevice(id));
CUDA_CHECK(cudaDeviceSynchronize());

if (src0_asq[id] > 0) {
ggml_cuda_pool_free(src0_ddq[id], src0_asq[id]);
}
Expand Down Expand Up @@ -2296,7 +2292,7 @@ void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tensor * sr
const int64_t ne02 = src0->ne[2];

CUDA_CHECK(cudaSetDevice(g_main_device));
cudaStream_t cudaStream_main = g_cudaStreams_main[g_main_device][0];
cudaStream_t cudaStream_main = g_cudaStreams_main[g_main_device];

struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
void * src0_ddq = src0_extra->data_device[g_main_device];
Expand All @@ -2308,8 +2304,6 @@ void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tensor * sr
float * dst_ddf = (float *) dst_extra->data_device[g_main_device];

ggml_mul_mat_p021_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, cudaStream_main);

CUDA_CHECK(cudaDeviceSynchronize());
}

void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
Expand All @@ -2327,7 +2321,7 @@ void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor * src1
const int64_t nb02 = src0->nb[2];

CUDA_CHECK(cudaSetDevice(g_main_device));
cudaStream_t cudaStream_main = g_cudaStreams_main[g_main_device][0];
cudaStream_t cudaStream_main = g_cudaStreams_main[g_main_device];

struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
void * src0_ddq = src0_extra->data_device[g_main_device];
Expand All @@ -2342,8 +2336,6 @@ void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor * src1
const int channel_stride_x = nb02 / sizeof(half);

ggml_mul_mat_vec_nc_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, channel_stride_x, cudaStream_main);

CUDA_CHECK(cudaDeviceSynchronize());
}

void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
Expand Down Expand Up @@ -2399,7 +2391,7 @@ void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tens
const int64_t nb12 = src1->nb[2];

CUDA_CHECK(cudaSetDevice(g_main_device));
cudaStream_t cudaStream_main = g_cudaStreams_main[g_main_device][0];
cudaStream_t cudaStream_main = g_cudaStreams_main[g_main_device];

const struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
const struct ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
Expand All @@ -2417,8 +2409,6 @@ void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tens
GGML_ASSERT(false);
}

CUDA_CHECK(cudaDeviceSynchronize());

(void) dst;
}

Expand Down
82 changes: 80 additions & 2 deletions ggml-metal.m
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Original file line number Diff line number Diff line change
Expand Up @@ -57,6 +57,7 @@
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(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);
Expand All @@ -66,8 +67,10 @@
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(alibi_f32);
GGML_METAL_DECL_KERNEL(cpy_f32_f16);
GGML_METAL_DECL_KERNEL(cpy_f32_f32);
GGML_METAL_DECL_KERNEL(cpy_f16_f16);

#undef GGML_METAL_DECL_KERNEL
};
Expand Down Expand Up @@ -162,6 +165,7 @@ @implementation GGMLMetalClass
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(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);
Expand All @@ -171,8 +175,10 @@ @implementation GGMLMetalClass
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(alibi_f32);
GGML_METAL_ADD_KERNEL(cpy_f32_f16);
GGML_METAL_ADD_KERNEL(cpy_f32_f32);
GGML_METAL_ADD_KERNEL(cpy_f16_f16);

#undef GGML_METAL_ADD_KERNEL
}
Expand Down Expand Up @@ -250,10 +256,10 @@ bool ggml_metal_add_buffer(
if (ctx->buffers[ctx->n_buffers].metal == nil) {
fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, aligned_size / 1024.0 / 1024.0);
return false;
} else {
fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB\n", __func__, name, aligned_size / 1024.0 / 1024.0);
}

fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB\n", __func__, name, aligned_size / 1024.0 / 1024.0);

++ctx->n_buffers;
}

Expand Down Expand Up @@ -735,6 +741,70 @@ void ggml_metal_graph_compute(

[encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_NORM:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}

const float eps = 1e-5f;

const int nth = 256;

[encoder setComputePipelineState:ctx->pipeline_norm];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3];
[encoder setBytes:&eps length:sizeof( float) atIndex:4];
[encoder setThreadgroupMemoryLength:nth*sizeof(float) atIndex:0];

const int64_t nrows = ggml_nrows(src0);

[encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_ALIBI:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}

GGML_ASSERT((src0t == GGML_TYPE_F32));

const int n_past = ((int32_t *) src1->data)[0]; UNUSED(n_past);
const int n_head = ((int32_t *) src1->data)[1];
const float max_bias = ((float *) src1->data)[2];

if (__builtin_popcount(n_head) != 1) {
GGML_ASSERT(false && "only power-of-two n_head implemented");
}

const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);

[encoder setComputePipelineState:ctx->pipeline_alibi_f32];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
[encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3];
[encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4];
[encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5];
[encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7];
[encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8];
[encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9];
[encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10];
[encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11];
[encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12];
[encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13];
[encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14];
[encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15];
[encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16];
[encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17];
[encoder setBytes:&m0 length:sizeof( float) atIndex:18];
const int nth = 32;
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_ROPE:
{
if (encoder == nil) {
Expand Down Expand Up @@ -788,6 +858,14 @@ void ggml_metal_graph_compute(
default: GGML_ASSERT(false && "not implemented");
};
} break;
case GGML_TYPE_F16:
{
switch (dstt) {
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_cpy_f16_f16]; break;
case GGML_TYPE_F32: GGML_ASSERT(false && "cpy_f16_f32 not implemented"); break;
default: GGML_ASSERT(false && "not implemented");
};
} break;
default: GGML_ASSERT(false && "not implemented");
}

Expand Down
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