root/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-11-11 13:30:35 +00:00
Code Issues Actions 11 Packages Projects Releases Wiki Activity

llama : add Mixtral support (#4406)

Browse Source 
* convert : support Mixtral as LLAMA arch

* convert : fix n_ff typo

* llama : model loading

* ggml : sync latest ggml_mul_mat_id

* llama : update graph to support MoE

* llama : fix cur -> cur_expert

* llama : first working version

* llama : fix expert weighting in the FFN

* ggml : ggml_get_rows support 2D indexing [n_tokens, n_experts] (cpu only)

* ggml : add n_as argument to ggml_mul_mat_id

* ggml : fix ggml_get_rows to take into account ne02 / ne11

* metal : add more general support for ggml_get_rows + tests

* llama : add basic support for offloading moe with CUDA

* metal : add/mul/div use general kernel when src1 not cont

* metal : reduce the kernel launches for ggml_mul_mat_id

* ggml : get_rows : support non-contiguos tensors with gaps, generalize up to 3D

* ggml : update get_rows f16 and q

* cuda : support non-contiguous src1 in get_rows

* llama : offload missing ffn_moe_silu

* metal : fix ggml_get_rows to work with non-cont src1

* metal : add indirect mat-vec kernels for all quantization types

* llama : do not quantize expert gating tensors

* llama : add n_expert and n_expert_used to hparams + change quants

* test-backend-ops : add moe test

* cuda : fix get_rows when ncols is odd

* convert : determine n_ctx correctly

* metal : fix ggml_mul_mat_id for F32

* test-backend-ops : make experts more evenly probable (test_moe)

* test-backend-ops : cleanup, add moe test for batches

* test-backend-ops : add cpy from f32 -> all types test

* test-backend-ops : fix dequantize block offset

* llama : fix hard-coded number of experts

* test-backend-ops : simplify and disable slow tests to avoid CI timeout

* test-backend-ops : disable MOE test with thread sanitizer

* cuda : fix mul_mat_id with multi gpu

* convert : use 1e6 rope_freq_base for mixtral

* convert : fix style

* convert : support safetensors format

* gguf-py : bump version

* metal : add cpy f16 -> f32 kernel

* metal : fix binary ops for ne10 % 4 != 0

* test-backend-ops : add one more sum_rows test

* ggml : do not use BLAS with ggml_mul_mat_id

* convert-hf : support for mixtral-instruct (#4428)

* convert : typo fix, add additional hyperparameters, use LLaMA arch for Mixtral-instruct

* convert : use sentencepiece tokenizer for Mixtral-instruct

* convert : make flake8 happy

* metal : fix soft_max kernels

ref: 1914017863

* metal : limit kernels to not use more than the allowed threads

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Radek Pilar <github@mrkva.eu>
This commit is contained in:
slaren 2023-12-13 13:04:25 +01:00 committed by GitHub
parent fecac45658
commit 799a1cb13b
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
14 changed files with 2370 additions and 395 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
Makefile
View File

@ -399,6 +399,11 @@ ifdef LLAMA_CUBLAS
MK_LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib
OBJS += ggml-cuda.o
NVCCFLAGS = --forward-unknown-to-host-compiler -use_fast_math
ifdef LLAMA_DEBUG
NVCCFLAGS += -lineinfo
endif
ifdef LLAMA_CUDA_NVCC
NVCC = $(LLAMA_CUDA_NVCC)
else

21
convert-hf-to-gguf.py
View File

@ -77,8 +77,18 @@ class Model:
self.gguf_writer.add_embedding_length(n_embd)
if (n_ff := self.hparams.get("intermediate_size")) is not None:
self.gguf_writer.add_feed_forward_length(n_ff)
if (n_head := self.hparams.get("num_attention_head")) is not None:
if (n_head := self.hparams.get("num_attention_heads")) is not None:
self.gguf_writer.add_head_count(n_head)
if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
self.gguf_writer.add_head_count_kv(n_head_kv)
if (n_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
self.gguf_writer.add_layer_norm_rms_eps(n_rms_eps)
if (n_experts := self.hparams.get("num_local_experts")) is not None:
self.gguf_writer.add_expert_count(n_experts)
if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
self.gguf_writer.add_expert_used_count(n_experts_used)
self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
def write_tensors(self):
@ -170,6 +180,8 @@ class Model:
return StableLMModel
if model_architecture == "QWenLMHeadModel":
return QwenModel
if model_architecture == "MixtralForCausalLM":
return MixtralModel
return Model
def _is_model_safetensors(self) -> bool:
@ -207,6 +219,8 @@ class Model:
return gguf.MODEL_ARCH.STABLELM
if arch == "QWenLMHeadModel":
return gguf.MODEL_ARCH.QWEN
if arch == "MixtralForCausalLM":
return gguf.MODEL_ARCH.LLAMA
raise NotImplementedError(f'Architecture "{arch}" not supported!')
@ -837,6 +851,11 @@ class StableLMModel(Model):
self.gguf_writer.add_layer_norm_eps(1e-5)
class MixtralModel(Model):
def set_vocab(self):
self._set_vocab_sentencepiece()
class QwenModel(Model):
@staticmethod
def token_bytes_to_string(b):

74
convert.py
View File

@ -42,6 +42,7 @@ NDArray: TypeAlias = 'np.ndarray[Any, Any]'
ARCH = gguf.MODEL_ARCH.LLAMA
DEFAULT_CONCURRENCY = 8
#
# data types
#
@ -62,10 +63,10 @@ class UnquantizedDataType(DataType):
pass
DT_F16 = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])
DT_F32 = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])
DT_I32 = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = [])
DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0'])
DT_F16 = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])
DT_F32 = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])
DT_I32 = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = [])
DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0'])
@dataclass(frozen=True)
@ -151,14 +152,16 @@ GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = {
@dataclass
class Params:
n_vocab: int
n_embd: int
n_layer: int
n_ctx: int
n_ff: int
n_head: int
n_head_kv: int
f_norm_eps: float
n_vocab: int
n_embd: int
n_layer: int
n_ctx: int
n_ff: int
n_head: int
n_head_kv: int
n_experts: int | None = None
n_experts_used: int | None = None
f_norm_eps: float | None = None
rope_scaling_type: gguf.RopeScalingType | None = None
f_rope_freq_base: float | None = None
@ -233,6 +236,13 @@ class Params:
raise Exception("failed to guess 'n_ctx'. This model is unknown or unsupported.\n"
"Suggestion: provide 'config.json' of the model in the same directory containing model files.")
n_experts = None
n_experts_used = None
if "num_local_experts" in config:
n_experts = config["num_local_experts"]
n_experts_used = config["num_experts_per_tok"]
return Params(
n_vocab = config["vocab_size"],
n_embd = config["hidden_size"],
@ -241,6 +251,8 @@ class Params:
n_ff = config["intermediate_size"],
n_head = (n_head := config["num_attention_heads"]),
n_head_kv = config.get("num_key_value_heads", n_head),
n_experts = n_experts,
n_experts_used = n_experts_used,
f_norm_eps = config["rms_norm_eps"],
f_rope_freq_base = config.get("rope_theta"),
rope_scaling_type = rope_scaling_type,
@ -255,8 +267,15 @@ class Params:
def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params:
config = json.load(open(config_path))
n_experts = None
n_experts_used = None
f_rope_freq_base = None
# hack to determine LLaMA v1 vs v2 vs CodeLlama
if config.get("rope_theta") == 1000000:
if config.get("moe"):
# Mixtral
n_ctx = 32768
elif config.get("rope_theta") == 1000000:
# CodeLlama
n_ctx = 16384
elif config["norm_eps"] == 1e-05:
@ -266,16 +285,27 @@ class Params:
# LLaMA v1
n_ctx = 2048
if "layers.0.feed_forward.w1.weight" in model:
n_ff = model["layers.0.feed_forward.w1.weight"].shape[0]
if config.get("moe"):
n_ff = model["layers.0.feed_forward.experts.0.w1.weight"].shape[0]
n_experts = config["moe"]["num_experts"]
n_experts_used = config["moe"]["num_experts_per_tok"]
f_rope_freq_base = 1e6
return Params(
n_vocab = model["tok_embeddings.weight"].shape[0],
n_embd = config["dim"],
n_layer = config["n_layers"],
n_ctx = n_ctx,
n_ff = model["layers.0.feed_forward.w1.weight"].shape[0],
n_ff = n_ff,
n_head = (n_head := config["n_heads"]),
n_head_kv = config.get("n_kv_heads", n_head),
n_experts = n_experts,
n_experts_used = n_experts_used,
f_norm_eps = config["norm_eps"],
f_rope_freq_base = config.get("rope_theta"),
f_rope_freq_base = config.get("rope_theta", f_rope_freq_base),
)
@staticmethod
@ -832,7 +862,17 @@ class OutputFile:
self.gguf.add_rope_dimension_count(params.n_embd // params.n_head)
self.gguf.add_head_count (params.n_head)
self.gguf.add_head_count_kv (params.n_head_kv)
self.gguf.add_layer_norm_rms_eps (params.f_norm_eps)
if params.n_experts:
self.gguf.add_expert_count(params.n_experts)
if params.n_experts_used:
self.gguf.add_expert_used_count(params.n_experts_used)
if params.f_norm_eps:
self.gguf.add_layer_norm_rms_eps(params.f_norm_eps)
else:
raise ValueError('f_norm_eps is None')
if params.f_rope_freq_base is not None:
self.gguf.add_rope_freq_base(params.f_rope_freq_base)
@ -956,7 +996,7 @@ class OutputFile:
def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType:
wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) +".weight"].data_type
wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) + ".weight"].data_type
if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32):
return GGMLFileType.AllF32

297
ggml-cuda.cu
View File

@ -1,13 +1,15 @@
#include <algorithm>
#include <assert.h>
#include <atomic>
#include <cinttypes>
#include <cstddef>
#include <cstdint>
#include <cinttypes>
#include <float.h>
#include <limits>
#include <stdint.h>
#include <stdio.h>
#include <atomic>
#include <assert.h>
#include <vector>
#if defined(GGML_USE_HIPBLAS)
#include <hip/hip_runtime.h>
@ -1684,31 +1686,65 @@ static __global__ void quantize_q8_1(const float * __restrict__ x, void * __rest
}
template<int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
static __global__ void k_get_rows(const void * x, const int32_t * y, dst_t * dst, const int ncols) {
const int col = (blockIdx.x*blockDim.x + threadIdx.x)*2;
const int row = blockDim.y*blockIdx.y + threadIdx.y;
static __global__ void k_get_rows(
const void * src0, const int32_t * src1, dst_t * dst,
int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
/*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
/*size_t s0,*/ size_t s1, size_t s2, size_t s3,
/*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
size_t s10, size_t s11, size_t s12/*, size_t s13*/) {
if (col >= ncols) {
const int i00 = (blockIdx.x*blockDim.x + threadIdx.x)*2;
const int i10 = blockDim.y*blockIdx.y + threadIdx.y;
const int i11 = (blockIdx.z*blockDim.z + threadIdx.z)/ne12;
const int i12 = (blockIdx.z*blockDim.z + threadIdx.z)%ne12;
if (i00 >= ne00) {
return;
}
const int r = y[row];
const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
// copy x[r*ncols + col] to dst[row*ncols + col]
const int xi = r*ncols + col;
const int di = row*ncols + col;
dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
const void * src0_row = (const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03;
const int ib = xi/qk; // block index
const int iqs = (xi%qk)/qr; // quant index
const int iybs = di - di%qk; // y block start index
const int ib = i00/qk; // block index
const int iqs = (i00%qk)/qr; // quant index
const int iybs = i00 - i00%qk; // dst block start index
const int y_offset = qr == 1 ? 1 : qk/2;
// dequantize
dfloat2 v;
dequantize_kernel(x, ib, iqs, v);
dequantize_kernel(src0_row, ib, iqs, v);
dst[iybs + iqs + 0] = v.x;
dst[iybs + iqs + y_offset] = v.y;
dst_row[iybs + iqs + 0] = v.x;
dst_row[iybs + iqs + y_offset] = v.y;
}
template<typename src0_t, typename dst_t>
static __global__ void k_get_rows_float(
const src0_t * src0, const int32_t * src1, dst_t * dst,
int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
/*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
/*size_t s0,*/ size_t s1, size_t s2, size_t s3,
/*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
size_t s10, size_t s11, size_t s12/*, size_t s13*/) {
const int i00 = blockIdx.x*blockDim.x + threadIdx.x;
const int i10 = blockDim.y*blockIdx.y + threadIdx.y;
const int i11 = (blockIdx.z*blockDim.z + threadIdx.z)/ne12;
const int i12 = (blockIdx.z*blockDim.z + threadIdx.z)%ne12;
if (i00 >= ne00) {
return;
}
const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
const src0_t * src0_row = (const src0_t *)((const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03);
dst_row[i00] = src0_row[i00];
}
template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
@ -5053,11 +5089,69 @@ static __global__ void im2col_f32_f16(
}
template<int qk, int qr, dequantize_kernel_t dq>
static void get_rows_cuda(const void * x, const int32_t * y, float * dst, const int nrows, const int ncols, cudaStream_t stream) {
static void get_rows_cuda(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const void * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) {
GGML_TENSOR_BINARY_OP_LOCALS
const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
const int block_num_x = (ncols + 2*CUDA_GET_ROWS_BLOCK_SIZE - 1) / (2*CUDA_GET_ROWS_BLOCK_SIZE);
const dim3 block_nums(block_num_x, nrows, 1);
k_get_rows<qk, qr, dq><<<block_nums, block_dims, 0, stream>>>(x, y, dst, ncols);
const int block_num_x = (ne00 + 2*CUDA_GET_ROWS_BLOCK_SIZE - 1) / (2*CUDA_GET_ROWS_BLOCK_SIZE);
const dim3 block_nums(block_num_x, ne10, ne11*ne12);
// strides in elements
//const size_t s0 = nb0 / ggml_element_size(dst);
const size_t s1 = nb1 / ggml_element_size(dst);
const size_t s2 = nb2 / ggml_element_size(dst);
const size_t s3 = nb3 / ggml_element_size(dst);
const size_t s10 = nb10 / ggml_element_size(src1);
const size_t s11 = nb11 / ggml_element_size(src1);
const size_t s12 = nb12 / ggml_element_size(src1);
//const size_t s13 = nb13 / ggml_element_size(src1);
GGML_ASSERT(ne00 % 2 == 0);
k_get_rows<qk, qr, dq><<<block_nums, block_dims, 0, stream>>>(
src0_dd, src1_dd, dst_dd,
ne00, /*ne01, ne02, ne03,*/
/*ne10, ne11,*/ ne12, /*ne13,*/
/* s0,*/ s1, s2, s3,
/* nb00,*/ nb01, nb02, nb03,
s10, s11, s12/*, s13*/);
(void) dst;
}
template<typename src0_t>
static void get_rows_cuda_float(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const src0_t * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) {
GGML_TENSOR_BINARY_OP_LOCALS
const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
const int block_num_x = (ne00 + CUDA_GET_ROWS_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BLOCK_SIZE;
const dim3 block_nums(block_num_x, ne10, ne11*ne12);
// strides in elements
//const size_t s0 = nb0 / ggml_element_size(dst);
const size_t s1 = nb1 / ggml_element_size(dst);
const size_t s2 = nb2 / ggml_element_size(dst);
const size_t s3 = nb3 / ggml_element_size(dst);
const size_t s10 = nb10 / ggml_element_size(src1);
const size_t s11 = nb11 / ggml_element_size(src1);
const size_t s12 = nb12 / ggml_element_size(src1);
//const size_t s13 = nb13 / ggml_element_size(src1);
k_get_rows_float<<<block_nums, block_dims, 0, stream>>>(
src0_dd, src1_dd, dst_dd,
ne00, /*ne01, ne02, ne03,*/
/*ne10, ne11,*/ ne12, /*ne13,*/
/* s0,*/ s1, s2, s3,
/* nb00,*/ nb01, nb02, nb03,
s10, s11, s12/*, s13*/);
(void) dst;
}
template<float (*bin_op)(const float, const float)>
@ -5069,7 +5163,6 @@ struct bin_bcast_cuda {
GGML_TENSOR_BINARY_OP_LOCALS
int nr0 = ne10/ne0;
int nr1 = ne11/ne1;
int nr2 = ne12/ne2;
@ -5117,26 +5210,28 @@ struct bin_bcast_cuda {
int64_t ne12 = cne1[2];
int64_t ne13 = cne1[3];
//size_t nb0 = cnb0[0];
size_t nb0 = cnb0[0];
size_t nb1 = cnb0[1];
size_t nb2 = cnb0[2];
size_t nb3 = cnb0[3];
//size_t nb10 = cnb1[0];
size_t nb10 = cnb1[0];
size_t nb11 = cnb1[1];
size_t nb12 = cnb1[2];
size_t nb13 = cnb1[3];
//size_t s0 = nb0 / sizeof(src1_t);
size_t s0 = nb0 / sizeof(src1_t);
size_t s1 = nb1 / sizeof(src1_t);
size_t s2 = nb2 / sizeof(src1_t);
size_t s3 = nb3 / sizeof(src1_t);
//size_t s10 = nb10 / sizeof(src1_t);
size_t s10 = nb10 / sizeof(src1_t);
size_t s11 = nb11 / sizeof(src1_t);
size_t s12 = nb12 / sizeof(src1_t);
size_t s13 = nb13 / sizeof(src1_t);
GGML_ASSERT(s0 == 1);
GGML_ASSERT(s10 == 1);
const int block_size = 128;
@ -6447,36 +6542,34 @@ static void ggml_cuda_op_get_rows(
GGML_ASSERT(src1->type == GGML_TYPE_I32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));
GGML_ASSERT(ggml_is_contiguous(dst));
const int ncols = src0->ne[0];
const int nrows = ggml_nelements(src1);
GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type));
GGML_ASSERT(dst->nb[0] == ggml_type_size(dst->type));
const int32_t * src1_i32 = (const int32_t *) src1_d;
switch (src0->type) {
case GGML_TYPE_F16:
get_rows_cuda<1, 1, convert_f16>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
get_rows_cuda_float(src0, src1, dst, (const half *)src0_d, src1_i32, dst_d, stream);
break;
case GGML_TYPE_F32:
get_rows_cuda<1, 1, convert_f32>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
get_rows_cuda_float(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
break;
case GGML_TYPE_Q4_0:
get_rows_cuda<QK4_0, QR4_0, dequantize_q4_0>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
get_rows_cuda<QK4_0, QR4_0, dequantize_q4_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
break;
case GGML_TYPE_Q4_1:
get_rows_cuda<QK4_1, QR4_1, dequantize_q4_1>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
get_rows_cuda<QK4_1, QR4_1, dequantize_q4_1>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
break;
case GGML_TYPE_Q5_0:
get_rows_cuda<QK5_0, QR5_0, dequantize_q5_0>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
get_rows_cuda<QK5_0, QR5_0, dequantize_q5_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
break;
case GGML_TYPE_Q5_1:
get_rows_cuda<QK5_1, QR5_1, dequantize_q5_1>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
get_rows_cuda<QK5_1, QR5_1, dequantize_q5_1>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
break;
case GGML_TYPE_Q8_0:
get_rows_cuda<QK8_0, QR8_0, dequantize_q8_0>(src0_d, src1_i32, dst_d, nrows, ncols, stream);
get_rows_cuda<QK8_0, QR8_0, dequantize_q8_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
break;
default:
// TODO: k-quants
@ -8234,36 +8327,69 @@ static void ggml_cuda_mul_mat_id_cublas(ggml_tensor * dst) {
}
#endif
static void ggml_cuda_mul_mat_id(const ggml_tensor * _src0, const ggml_tensor * _src1, ggml_tensor * dst) {
static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
#if 0
//#ifdef CUDA_USE_TENSOR_CORES
// const bool use_tensor_cores = true;
//#else
// const bool use_tensor_cores = false;
//#endif
ggml_cuda_mul_mat_id_cublas(dst);
// TODO: mmq/mmv support
#else
const struct ggml_tensor * ids = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
const int id = dst->op_params[0];
int32_t * ids_dev = (int32_t *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device];
int32_t a_id;
CUDA_CHECK(cudaMemcpyAsync(&a_id, ids_dev + id, sizeof(int32_t), cudaMemcpyDeviceToHost, g_cudaStreams[g_main_device][0]));
CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[g_main_device][0]));
GGML_ASSERT(a_id >= 0 && a_id < ids->ne[0]);
const struct ggml_tensor * src0 = dst->src[a_id + 2];
ggml_cuda_mul_mat(src0, src1, dst);
#endif
(void) _src0;
(void) _src1;
GGML_ASSERT(dst->backend == GGML_BACKEND_GPU);
const struct ggml_tensor * ids = src0;
const int32_t id = ((int32_t *) dst->op_params)[0];
const int32_t n_as = ((int32_t *) dst->op_params)[1];
std::vector<char> ids_host(ggml_nbytes(ids));
if (ids->backend == GGML_BACKEND_GPU) {
const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device];
CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, g_cudaStreams[g_main_device][0]));
CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[g_main_device][0]));
} else {
memcpy(ids_host.data(), ids->data, ggml_nbytes(ids));
}
const ggml_tensor_extra_gpu * src1_extra = (const ggml_tensor_extra_gpu *) src1->extra;
const ggml_tensor_extra_gpu * dst_extra = (const ggml_tensor_extra_gpu *) dst->extra;
ggml_tensor_extra_gpu src1_row_extra;
ggml_tensor_extra_gpu dst_row_extra;
ggml_tensor src1_row = *src1;
ggml_tensor dst_row = *dst;
src1_row.ne[1] = 1;
dst_row.ne[1] = 1;
src1_row.nb[2] = src1_row.nb[1];
dst_row.nb[2] = dst_row.nb[1];
src1_row.nb[3] = src1_row.nb[1];
dst_row.nb[3] = dst_row.nb[1];
src1_row.extra = &src1_row_extra;
dst_row.extra = &dst_row_extra;
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
//int32_t row_id;
//CUDA_CHECK(cudaMemcpyAsync(&row_id, ids_dev + i01*ids->nb[1] + id*ids->nb[0], sizeof(int32_t), cudaMemcpyDeviceToHost, g_cudaStreams[g_main_device][0]));
//CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[g_main_device][0]));
const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
GGML_ASSERT(row_id >= 0 && row_id < n_as);
const struct ggml_tensor * src0_row = dst->src[row_id + 2];
src1_row_extra.data_device[g_main_device] = (char *) src1_extra->data_device[g_main_device] + i01*src1->nb[1];
src1_row.data = (char *) src1->data + i01*src1->nb[1];
dst_row_extra.data_device[g_main_device] = (char *) dst_extra->data_device[g_main_device] + i01*dst->nb[1];
dst_row.data = (char *) dst->data + i01*dst->nb[1];
ggml_cuda_mul_mat(src0_row, &src1_row, &dst_row);
}
}
static void ggml_cuda_scale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
@ -9181,6 +9307,45 @@ static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_ten
}
return true;
} break;
case GGML_OP_GET_ROWS:
{
switch (op->src[0]->type) {
case GGML_TYPE_F16:
case GGML_TYPE_F32:
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
return true;
default:
return false;
}
} break;
case GGML_OP_CPY:
{
ggml_type src0_type = op->src[0]->type;
ggml_type src1_type = op->src[1]->type;
if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
return true;
}
if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) {
return true;
}
if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q8_0) {
return true;
}
if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_0) {
return true;
}
if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_1) {
return true;
}
if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
return true;
}
return false;
} break;
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
@ -9188,7 +9353,6 @@ static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_ten
case GGML_OP_TRANSPOSE:
case GGML_OP_NORM:
case GGML_OP_REPEAT:
case GGML_OP_GET_ROWS:
case GGML_OP_DUP:
case GGML_OP_ADD:
case GGML_OP_MUL:
@ -9197,7 +9361,6 @@ static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_ten
case GGML_OP_SCALE:
case GGML_OP_SQR:
case GGML_OP_CLAMP:
case GGML_OP_CPY:
case GGML_OP_CONT:
case GGML_OP_DIAG_MASK_INF:
case GGML_OP_SOFT_MAX:
@ -9264,7 +9427,9 @@ static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * use
UNUSED(params);
}
extern "C" int ggml_backend_cuda_reg_devices() {
extern "C" int ggml_backend_cuda_reg_devices();
int ggml_backend_cuda_reg_devices() {
int device_count = ggml_cuda_get_device_count();
//int device_count = 1; // DEBUG: some tools require delaying CUDA initialization
for (int i = 0; i < device_count; i++) {

332
ggml-metal.m
View File

@ -102,6 +102,21 @@ struct ggml_metal_context {
GGML_METAL_DECL_KERNEL(mul_mv_q4_K_f32);
GGML_METAL_DECL_KERNEL(mul_mv_q5_K_f32);
GGML_METAL_DECL_KERNEL(mul_mv_q6_K_f32);
GGML_METAL_DECL_KERNEL(mul_mv_id_f32_f32);
//GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f16);
GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f32);
//GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f32_1row);
//GGML_METAL_DECL_KERNEL(mul_mv_id_f16_f32_l4);
GGML_METAL_DECL_KERNEL(mul_mv_id_q4_0_f32);
GGML_METAL_DECL_KERNEL(mul_mv_id_q4_1_f32);
GGML_METAL_DECL_KERNEL(mul_mv_id_q5_0_f32);
GGML_METAL_DECL_KERNEL(mul_mv_id_q5_1_f32);
GGML_METAL_DECL_KERNEL(mul_mv_id_q8_0_f32);
GGML_METAL_DECL_KERNEL(mul_mv_id_q2_K_f32);
GGML_METAL_DECL_KERNEL(mul_mv_id_q3_K_f32);
GGML_METAL_DECL_KERNEL(mul_mv_id_q4_K_f32);
GGML_METAL_DECL_KERNEL(mul_mv_id_q5_K_f32);
GGML_METAL_DECL_KERNEL(mul_mv_id_q6_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_f32_f32);
GGML_METAL_DECL_KERNEL(mul_mm_f16_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q4_0_f32);
@ -140,6 +155,7 @@ struct ggml_metal_context {
//GGML_METAL_DECL_KERNEL(cpy_f32_q5_0);
//GGML_METAL_DECL_KERNEL(cpy_f32_q5_1);
GGML_METAL_DECL_KERNEL(cpy_f16_f16);
GGML_METAL_DECL_KERNEL(cpy_f16_f32);
GGML_METAL_DECL_KERNEL(concat);
GGML_METAL_DECL_KERNEL(sqr);
GGML_METAL_DECL_KERNEL(sum_rows);
@ -177,6 +193,8 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
ggml_metal_log_callback(level, buffer, ggml_metal_log_user_data);
} else {
char* buffer2 = malloc(len+1);
va_end(args);
va_start(args, format);
vsnprintf(buffer2, len+1, format, args);
buffer2[len] = 0;
ggml_metal_log_callback(level, buffer2, ggml_metal_log_user_data);
@ -352,6 +370,21 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(mul_mv_q4_K_f32);
GGML_METAL_ADD_KERNEL(mul_mv_q5_K_f32);
GGML_METAL_ADD_KERNEL(mul_mv_q6_K_f32);
GGML_METAL_ADD_KERNEL(mul_mv_id_f32_f32);
//GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f16);
GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f32);
//GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f32_1row);
//GGML_METAL_ADD_KERNEL(mul_mv_id_f16_f32_l4);
GGML_METAL_ADD_KERNEL(mul_mv_id_q4_0_f32);
GGML_METAL_ADD_KERNEL(mul_mv_id_q4_1_f32);
GGML_METAL_ADD_KERNEL(mul_mv_id_q5_0_f32);
GGML_METAL_ADD_KERNEL(mul_mv_id_q5_1_f32);
GGML_METAL_ADD_KERNEL(mul_mv_id_q8_0_f32);
GGML_METAL_ADD_KERNEL(mul_mv_id_q2_K_f32);
GGML_METAL_ADD_KERNEL(mul_mv_id_q3_K_f32);
GGML_METAL_ADD_KERNEL(mul_mv_id_q4_K_f32);
GGML_METAL_ADD_KERNEL(mul_mv_id_q5_K_f32);
GGML_METAL_ADD_KERNEL(mul_mv_id_q6_K_f32);
if ([ctx->device supportsFamily:MTLGPUFamilyApple7]) {
GGML_METAL_ADD_KERNEL(mul_mm_f32_f32);
GGML_METAL_ADD_KERNEL(mul_mm_f16_f32);
@ -392,6 +425,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
//GGML_METAL_ADD_KERNEL(cpy_f32_q5_0);
//GGML_METAL_ADD_KERNEL(cpy_f32_q5_1);
GGML_METAL_ADD_KERNEL(cpy_f16_f16);
GGML_METAL_ADD_KERNEL(cpy_f16_f32);
GGML_METAL_ADD_KERNEL(concat);
GGML_METAL_ADD_KERNEL(sqr);
GGML_METAL_ADD_KERNEL(sum_rows);
@ -452,6 +486,21 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
GGML_METAL_DEL_KERNEL(mul_mv_q4_K_f32);
GGML_METAL_DEL_KERNEL(mul_mv_q5_K_f32);
GGML_METAL_DEL_KERNEL(mul_mv_q6_K_f32);
GGML_METAL_DEL_KERNEL(mul_mv_id_f32_f32);
//GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f16);
GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f32);
//GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f32_1row);
//GGML_METAL_DEL_KERNEL(mul_mv_id_f16_f32_l4);
GGML_METAL_DEL_KERNEL(mul_mv_id_q4_0_f32);
GGML_METAL_DEL_KERNEL(mul_mv_id_q4_1_f32);
GGML_METAL_DEL_KERNEL(mul_mv_id_q5_0_f32);
GGML_METAL_DEL_KERNEL(mul_mv_id_q5_1_f32);
GGML_METAL_DEL_KERNEL(mul_mv_id_q8_0_f32);
GGML_METAL_DEL_KERNEL(mul_mv_id_q2_K_f32);
GGML_METAL_DEL_KERNEL(mul_mv_id_q3_K_f32);
GGML_METAL_DEL_KERNEL(mul_mv_id_q4_K_f32);
GGML_METAL_DEL_KERNEL(mul_mv_id_q5_K_f32);
GGML_METAL_DEL_KERNEL(mul_mv_id_q6_K_f32);
if ([ctx->device supportsFamily:MTLGPUFamilyApple7]) {
GGML_METAL_DEL_KERNEL(mul_mm_f32_f32);
GGML_METAL_DEL_KERNEL(mul_mm_f16_f32);
@ -492,6 +541,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
//GGML_METAL_DEL_KERNEL(cpy_f32_q5_0);
//GGML_METAL_DEL_KERNEL(cpy_f32_q5_1);
GGML_METAL_DEL_KERNEL(cpy_f16_f16);
GGML_METAL_DEL_KERNEL(cpy_f16_f32);
GGML_METAL_DEL_KERNEL(concat);
GGML_METAL_DEL_KERNEL(sqr);
GGML_METAL_DEL_KERNEL(sum_rows);
@ -803,8 +853,9 @@ static bool ggml_metal_supports_op(const struct ggml_tensor * op) {
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_TRANSPOSE:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
case GGML_OP_GET_ROWS:
case GGML_OP_CONCAT:
case GGML_OP_ADD:
case GGML_OP_MUL:
@ -819,14 +870,38 @@ static bool ggml_metal_supports_op(const struct ggml_tensor * op) {
case GGML_OP_ROPE:
case GGML_OP_IM2COL:
case GGML_OP_ARGSORT:
case GGML_OP_DUP:
case GGML_OP_CPY:
case GGML_OP_CONT:
case GGML_OP_MUL_MAT:
case GGML_OP_MUL_MAT_ID:
return true;
case GGML_OP_CPY:
case GGML_OP_DUP:
case GGML_OP_CONT:
{
switch (op->src[0]->type) {
case GGML_TYPE_F32:
switch (op->type) {
case GGML_TYPE_F16:
case GGML_TYPE_F32:
case GGML_TYPE_Q8_0:
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
return true;
default:
return false;
}
case GGML_TYPE_F16:
switch (op->type) {
case GGML_TYPE_F16:
case GGML_TYPE_F32:
return true;
default:
return false;
}
default:
return false;
};
}
case GGML_OP_DIAG_MASK_INF:
case GGML_OP_GET_ROWS:
{
return op->ne[0] % 4 == 0;
}
@ -1001,34 +1076,37 @@ void ggml_metal_graph_compute(
case GGML_OP_MUL:
case GGML_OP_DIV:
{
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));
bool bcast_row = false;
int64_t nb = ne00;
if (ggml_nelements(src1) == ne10 && ne00 % 4 == 0) {
id<MTLComputePipelineState> pipeline = nil;
if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) {
GGML_ASSERT(ggml_is_contiguous(src0));
// src1 is a row
GGML_ASSERT(ne11 == 1);
nb = ne00 / 4;
switch (dst->op) {
case GGML_OP_ADD: [encoder setComputePipelineState:ctx->pipeline_add_row]; break;
case GGML_OP_MUL: [encoder setComputePipelineState:ctx->pipeline_mul_row]; break;
case GGML_OP_DIV: [encoder setComputePipelineState:ctx->pipeline_div_row]; break;
case GGML_OP_ADD: pipeline = ctx->pipeline_add_row; break;
case GGML_OP_MUL: pipeline = ctx->pipeline_mul_row; break;
case GGML_OP_DIV: pipeline = ctx->pipeline_div_row; break;
default: GGML_ASSERT(false);
}
bcast_row = true;
} else {
switch (dst->op) {
case GGML_OP_ADD: [encoder setComputePipelineState:ctx->pipeline_add]; break;
case GGML_OP_MUL: [encoder setComputePipelineState:ctx->pipeline_mul]; break;
case GGML_OP_DIV: [encoder setComputePipelineState:ctx->pipeline_div]; break;
case GGML_OP_ADD: pipeline = ctx->pipeline_add; break;
case GGML_OP_MUL: pipeline = ctx->pipeline_mul; break;
case GGML_OP_DIV: pipeline = ctx->pipeline_div; break;
default: GGML_ASSERT(false);
}
}
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
@ -1063,7 +1141,7 @@ void ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} else {
const int nth = MIN(1024, ne0);
const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0);
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
}
@ -1193,7 +1271,11 @@ void ggml_metal_graph_compute(
const float scale = ((float *) dst->op_params)[0];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
if (id_src1) {
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
} else {
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
}
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
@ -1444,7 +1526,7 @@ void ggml_metal_graph_compute(
else if (src0t == GGML_TYPE_Q6_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} else {
int64_t ny = (ne11 + nrows - 1)/nrows;
const int64_t ny = (ne11 + nrows - 1)/nrows;
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
}
@ -1456,7 +1538,7 @@ void ggml_metal_graph_compute(
GGML_ASSERT(src0t == GGML_TYPE_I32);
const int n_as = ne00;
const int n_as = ((int32_t *) dst->op_params)[1];
// TODO: make this more general
GGML_ASSERT(n_as <= 8);
@ -1488,14 +1570,22 @@ void ggml_metal_graph_compute(
// find the break-even point where the matrix-matrix kernel becomes more efficient compared
// to the matrix-vector kernel
int ne11_mm_min = 0;
int ne11_mm_min = 1;
const int idx = ((int32_t *) dst->op_params)[0];
// batch size
GGML_ASSERT(ne01 == ne11);
const int64_t _ne1 = 1; // kernel_mul_mm_impl needs a reference in constant memory
// for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
// AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
if ([ctx->device supportsFamily:MTLGPUFamilyApple7] &&
ne11 > ne11_mm_min) {
// !!!
// TODO: for now, always use mat-vec kernels until we figure out how to improve the
// indirect matrix multiplication
// !!!
if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && _ne1 > ne11_mm_min) {
switch (src2->type) {
case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_f32_f32]; break;
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_f16_f32]; break;
@ -1514,19 +1604,22 @@ void ggml_metal_graph_compute(
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&ne20 length:sizeof(ne20) atIndex:3];
[encoder setBytes:&ne22 length:sizeof(ne22) atIndex:4];
[encoder setBytes:&nb21 length:sizeof(nb21) atIndex:5];
[encoder setBytes:&nb22 length:sizeof(nb22) atIndex:6];
[encoder setBytes:&ne12 length:sizeof(ne12) atIndex:7];
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:8];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:9];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:10];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:11];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:12];
[encoder setBytes:&r2 length:sizeof(r2) atIndex:13];
[encoder setBytes:&r3 length:sizeof(r3) atIndex:14];
[encoder setBytes:&idx length:sizeof(idx) atIndex:15];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:3];
[encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4];
[encoder setBytes:&ne22 length:sizeof(ne22) atIndex:5];
[encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6];
[encoder setBytes:&nb22 length:sizeof(nb22) atIndex:7];
[encoder setBytes:&ne12 length:sizeof(ne12) atIndex:8];
[encoder setBytes:&ne13 length:sizeof(ne13) atIndex:9];
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:10];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:11];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:12];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13];
[encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:14];
[encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15];
[encoder setBytes:&r2 length:sizeof(r2) atIndex:16];
[encoder setBytes:&r3 length:sizeof(r3) atIndex:17];
[encoder setBytes:&idx length:sizeof(idx) atIndex:18];
// TODO: how to make this an array? read Metal docs
for (int j = 0; j < n_as; ++j) {
struct ggml_tensor * src_cur = dst->src[2 + j];
@ -1534,11 +1627,157 @@ void ggml_metal_graph_compute(
size_t offs_src_cur = 0;
id<MTLBuffer> id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur);
[encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:16 + j];
[encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:19 + j];
}
[encoder setThreadgroupMemoryLength:8192 atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne21 + 63)/64, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
// TODO: processing one row at a time (ne11 -> 1) is not efficient
[encoder dispatchThreadgroups:MTLSizeMake( (_ne1 + 31)/32, (ne21 + 63)/64, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
} else {
int nth0 = 32;
int nth1 = 1;
int nrows = 1;
//printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
// use custom matrix x vector kernel
switch (src2t) {
case GGML_TYPE_F32:
{
GGML_ASSERT(src1t == GGML_TYPE_F32);
[encoder setComputePipelineState:ctx->pipeline_mul_mv_id_f32_f32];
} break;
case GGML_TYPE_F16:
{
GGML_ASSERT(src1t == GGML_TYPE_F32);
nth0 = 32;
nth1 = 1;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_id_f16_f32];
} break;
case GGML_TYPE_Q4_0:
{
nth0 = 8;
nth1 = 8;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q4_0_f32];
} break;
case GGML_TYPE_Q4_1:
{
nth0 = 8;
nth1 = 8;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q4_1_f32];
} break;
case GGML_TYPE_Q5_0:
{
nth0 = 8;
nth1 = 8;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q5_0_f32];
} break;
case GGML_TYPE_Q5_1:
{
nth0 = 8;
nth1 = 8;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q5_1_f32];
} break;
case GGML_TYPE_Q8_0:
{
nth0 = 8;
nth1 = 8;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q8_0_f32];
} break;
case GGML_TYPE_Q2_K:
{
nth0 = 2;
nth1 = 32;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q2_K_f32];
} break;
case GGML_TYPE_Q3_K:
{
nth0 = 2;
nth1 = 32;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q3_K_f32];
} break;
case GGML_TYPE_Q4_K:
{
nth0 = 4; //1;
nth1 = 8; //32;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q4_K_f32];
} break;
case GGML_TYPE_Q5_K:
{
nth0 = 2;
nth1 = 32;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q5_K_f32];
} break;
case GGML_TYPE_Q6_K:
{
nth0 = 2;
nth1 = 32;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_id_q6_K_f32];
} break;
default:
{
GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t);
GGML_ASSERT(false && "not implemented");
}
};
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:3];
[encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4];
[encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5];
[encoder setBytes:&ne22 length:sizeof(ne22) atIndex:6];
[encoder setBytes:&nb20 length:sizeof(nb20) atIndex:7];
[encoder setBytes:&nb21 length:sizeof(nb21) atIndex:8];
[encoder setBytes:&nb22 length:sizeof(nb22) atIndex:9];
[encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10];
[encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:11];
[encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12];
[encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13];
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:17];
[encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:18];
[encoder setBytes:&nb1 length:sizeof(nb1) atIndex:19];
[encoder setBytes:&r2 length:sizeof(r2) atIndex:20];
[encoder setBytes:&r3 length:sizeof(r3) atIndex:21];
[encoder setBytes:&idx length:sizeof(idx) atIndex:22];
// TODO: how to make this an array? read Metal docs
for (int j = 0; j < n_as; ++j) {
struct ggml_tensor * src_cur = dst->src[2 + j];
size_t offs_src_cur = 0;
id<MTLBuffer> id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur);
[encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:23 + j];
}
if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 ||
src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 ||
src2t == GGML_TYPE_Q2_K) { // || src2t == GGML_TYPE_Q4_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src2t == GGML_TYPE_Q4_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src2t == GGML_TYPE_Q3_K) {
#ifdef GGML_QKK_64
[encoder dispatchThreadgroups:MTLSizeMake((ne21 + 1)/2, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
#else
[encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
#endif
}
else if (src2t == GGML_TYPE_Q5_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src2t == GGML_TYPE_Q6_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne21 + 1)/2, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} else {
const int64_t ny = (_ne1 + nrows - 1)/nrows;
[encoder dispatchThreadgroups:MTLSizeMake(ne21, ny, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
}
} break;
case GGML_OP_GET_ROWS:
@ -1559,16 +1798,19 @@ void ggml_metal_graph_compute(
default: GGML_ASSERT(false && "not implemented");
}
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:4];
[encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:5];
[encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:5];
[encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:6];
[encoder setBytes:&nb10 length:sizeof( int64_t) atIndex:7];
[encoder setBytes:&nb11 length:sizeof( int64_t) atIndex:8];
[encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:9];
[encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:10];
const int64_t n = ggml_nelements(src1);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake(ne10, ne11, 1) threadsPerThreadgroup:MTLSizeMake(32, 1, 1)];
} break;
case GGML_OP_RMS_NORM:
{
@ -1813,7 +2055,7 @@ void ggml_metal_graph_compute(
{
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;
case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_cpy_f16_f32]; break;
default: GGML_ASSERT(false && "not implemented");
};
} break;

1320
ggml-metal.metal
View File

File diff suppressed because it is too large Load Diff

168
ggml.c
View File

@ -4075,17 +4075,18 @@ struct ggml_tensor * ggml_mul_mat(
struct ggml_tensor * ggml_mul_mat_id(
struct ggml_context * ctx,
struct ggml_tensor * as[],
struct ggml_tensor * const as[],
int n_as,
struct ggml_tensor * ids,
int id,
struct ggml_tensor * b) {
int64_t n_as = ids->ne[0];
GGML_ASSERT(ids->type == GGML_TYPE_I32);
GGML_ASSERT(ggml_is_vector(ids));
GGML_ASSERT(ids->ne[2] == 1 && ids->ne[3] == 1);
GGML_ASSERT(ids->ne[1] == b->ne[1]);
GGML_ASSERT(ids->ne[2] == b->ne[2] && ids->ne[3] == b->ne[3]);
GGML_ASSERT(n_as > 0 && n_as <= GGML_MAX_SRC - 2);
GGML_ASSERT(id >= 0 && id < n_as);
GGML_ASSERT(id >= 0 && id < ids->ne[0]);
bool is_node = false;
@ -4097,13 +4098,14 @@ struct ggml_tensor * ggml_mul_mat_id(
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, MAX(as[0]->n_dims, b->n_dims), ne);
ggml_set_op_params_i32(result, 0, id);
ggml_set_op_params_i32(result, 1, n_as);
result->op = GGML_OP_MUL_MAT_ID;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = ids;
result->src[1] = b;
for (int64_t i = 0; i < n_as; i++) {
for (int i = 0; i < n_as; i++) {
struct ggml_tensor * a = as[i];
GGML_ASSERT(ggml_are_same_shape(as[0], a));
GGML_ASSERT(ggml_can_mul_mat(a, b));
@ -4731,7 +4733,9 @@ struct ggml_tensor * ggml_get_rows(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b) {
GGML_ASSERT(ggml_is_matrix(a) && ggml_is_vector(b) && b->type == GGML_TYPE_I32);
GGML_ASSERT(a->ne[2] == b->ne[1]);
GGML_ASSERT(b->ne[3] == 1);
GGML_ASSERT(b->type == GGML_TYPE_I32);
bool is_node = false;
@ -4741,7 +4745,7 @@ struct ggml_tensor * ggml_get_rows(
// TODO: implement non F32 return
//struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]);
struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, a->ne[0], b->ne[0]);
struct ggml_tensor * result = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, a->ne[0], b->ne[0], b->ne[1], b->ne[2]);
result->op = GGML_OP_GET_ROWS;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -9504,8 +9508,11 @@ static bool ggml_compute_forward_mul_mat_use_blas(
const int64_t ne0 = dst->ne[0];
const int64_t ne1 = dst->ne[1];
// NOTE: with GGML_OP_MUL_MAT_ID we don't want to go through the BLAS branch because it will dequantize (to_float)
// all the experts for each batch element and the processing would become incredibly slow
// TODO: find the optimal values for these
if (ggml_is_contiguous(src0) &&
if (dst->op != GGML_OP_MUL_MAT_ID &&
ggml_is_contiguous(src0) &&
ggml_is_contiguous(src1) &&
//src0->type == GGML_TYPE_F32 &&
src1->type == GGML_TYPE_F32 &&
@ -9519,11 +9526,16 @@ static bool ggml_compute_forward_mul_mat_use_blas(
}
#endif
// off1 = offset in i11 and i1
// cne1 = ne11 and ne1
// in a normal matrix multiplication, off1 = 0 and cne1 = ne1
// during GGML_TASK_INIT, the full src1 is converted regardless of off1 and cne1
static void ggml_compute_forward_mul_mat(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
struct ggml_tensor * dst,
int64_t off1, int64_t cne1) {
int64_t t0 = ggml_perf_time_us();
UNUSED(t0);
@ -9591,10 +9603,9 @@ static void ggml_compute_forward_mul_mat(
const int64_t i03 = i13/r3;
const int64_t i02 = i12/r2;
const void * x = (char *) src0->data + i02*nb02 + i03*nb03;
const float * y = (float *) ((char *) src1->data + i12*nb12 + i13*nb13);
float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
const void * x = (char *) src0->data + i02*nb02 + i03*nb03;
const float * y = (float *) ((char *) src1->data + off1*nb11 + i12*nb12 + i13*nb13);
float * d = (float *) ((char *) dst->data + off1*nb1 + i12*nb2 + i13*nb3);
if (type != GGML_TYPE_F32) {
float * const wdata = params->wdata;
@ -9611,10 +9622,10 @@ static void ggml_compute_forward_mul_mat(
}
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
ne11, ne01, ne10,
1.0f, y, ne10,
x, ne00,
0.0f, d, ne01);
cne1, ne01, ne10,
1.0f, y, ne10,
x, ne00,
0.0f, d, ne01);
}
}
@ -9630,6 +9641,7 @@ static void ggml_compute_forward_mul_mat(
const size_t row_size = ne10*ggml_type_size(vec_dot_type)/ggml_blck_size(vec_dot_type);
assert(params->wsize >= ne11*ne12*ne13*row_size);
assert(src1->type == GGML_TYPE_F32);
for (int64_t i13 = 0; i13 < ne13; ++i13) {
for (int64_t i12 = 0; i12 < ne12; ++i12) {
@ -9652,7 +9664,7 @@ static void ggml_compute_forward_mul_mat(
const size_t row_size = ne10*ggml_type_size(vec_dot_type)/ggml_blck_size(vec_dot_type);
const int64_t nr0 = ne01; // src0 rows
const int64_t nr1 = ne11*ne12*ne13; // src1 rows
const int64_t nr1 = cne1*ne12*ne13; // src1 rows
//printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1);
@ -9694,9 +9706,9 @@ static void ggml_compute_forward_mul_mat(
for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) {
for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) {
for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) {
const int64_t i13 = (ir1/(ne12*ne11));
const int64_t i12 = (ir1 - i13*ne12*ne11)/ne11;
const int64_t i11 = (ir1 - i13*ne12*ne11 - i12*ne11);
const int64_t i13 = (ir1/(ne12*cne1));
const int64_t i12 = (ir1 - i13*ne12*cne1)/cne1;
const int64_t i11 = (ir1 - i13*ne12*cne1 - i12*cne1) + off1;
// broadcast src0 into src1
const int64_t i03 = i13/r3;
@ -9736,20 +9748,28 @@ static void ggml_compute_forward_mul_mat(
static void ggml_compute_forward_mul_mat_id(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
const struct ggml_tensor * ids = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
// during GGML_TASK_INIT the entire src1 is converted to vec_dot_type
ggml_compute_forward_mul_mat(params, dst->src[2], src1, dst, 0, dst->ne[1]);
return;
}
const int id = ggml_get_op_params_i32(dst, 0);
const struct ggml_tensor * ids = src0;
const int id = ggml_get_op_params_i32(dst, 0);
const int n_as = ggml_get_op_params_i32(dst, 1);
const int a_id = ((int32_t *)ids->data)[id];
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
const int32_t row_id = *(const int32_t *) ((const char *) ids->data + i01*ids->nb[1] + id*ids->nb[0]);
GGML_ASSERT(a_id >= 0 && a_id < ids->ne[0]);
GGML_ASSERT(row_id >= 0 && row_id < n_as);
const struct ggml_tensor * src0 = dst->src[a_id + 2];
ggml_compute_forward_mul_mat(params, src0, src1, dst);
const struct ggml_tensor * src0_row = dst->src[row_id + 2];
ggml_compute_forward_mul_mat(params, src0_row, src1, dst, i01, 1);
}
}
// ggml_compute_forward_out_prod
@ -10325,21 +10345,30 @@ static void ggml_compute_forward_get_rows_q(
return;
}
const int nc = src0->ne[0];
const int nr = ggml_nelements(src1);
GGML_TENSOR_BINARY_OP_LOCALS
const int64_t nc = ne00;
const int64_t nr = ggml_nelements(src1); GGML_UNUSED(nr);
const enum ggml_type type = src0->type;
ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
assert( dst->ne[0] == nc);
assert( dst->ne[1] == nr);
assert(src0->nb[0] == ggml_type_size(type));
assert(ne0 == nc);
assert(ne02 == ne11);
assert(nb00 == ggml_type_size(type));
assert(ggml_nrows(dst) == nr);
for (int i = 0; i < nr; ++i) {
const int r = ((int32_t *) src1->data)[i];
// TODO: multi-thread
for (int64_t i12 = 0; i12 < ne12; ++i12) {
for (int64_t i11 = 0; i11 < ne11; ++i11) {
for (int64_t i10 = 0; i10 < ne10; ++i10) {
const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
dequantize_row_q(
(const void *) ((char *) src0->data + r*src0->nb[1]),
(float *) ((char *) dst->data + i*dst->nb[1]), nc);
dequantize_row_q(
(const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
(float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc);
}
}
}
}
@ -10354,19 +10383,26 @@ static void ggml_compute_forward_get_rows_f16(
return;
}
const int nc = src0->ne[0];
const int nr = ggml_nelements(src1);
GGML_TENSOR_BINARY_OP_LOCALS
assert( dst->ne[0] == nc);
assert( dst->ne[1] == nr);
assert(src0->nb[0] == sizeof(ggml_fp16_t));
const int64_t nc = ne00;
const int64_t nr = ggml_nelements(src1); GGML_UNUSED(nr);
for (int i = 0; i < nr; ++i) {
const int r = ((int32_t *) src1->data)[i];
assert(ne0 == nc);
assert(ne02 == ne11);
assert(nb00 == sizeof(ggml_fp16_t));
assert(ggml_nrows(dst) == nr);
for (int j = 0; j < nc; ++j) {
ggml_fp16_t v = ((ggml_fp16_t *) ((char *) src0->data + r*src0->nb[1]))[j];
((float *) ((char *) dst->data + i*dst->nb[1]))[j] = GGML_FP16_TO_FP32(v);
// TODO: multi-thread
for (int64_t i12 = 0; i12 < ne12; ++i12) {
for (int64_t i11 = 0; i11 < ne11; ++i11) {
for (int64_t i10 = 0; i10 < ne10; ++i10) {
const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
ggml_fp16_to_fp32_row(
(const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03),
(float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc);
}
}
}
}
@ -10382,19 +10418,27 @@ static void ggml_compute_forward_get_rows_f32(
return;
}
const int nc = src0->ne[0];
const int nr = ggml_nelements(src1);
GGML_TENSOR_BINARY_OP_LOCALS
assert( dst->ne[0] == nc);
assert( dst->ne[1] == nr);
assert(src0->nb[0] == sizeof(float));
const int64_t nc = ne00;
const int64_t nr = ggml_nelements(src1); GGML_UNUSED(nr);
for (int i = 0; i < nr; ++i) {
const int r = ((int32_t *) src1->data)[i];
assert(ne0 == nc);
assert(ne02 == ne11);
assert(nb00 == sizeof(float));
assert(ggml_nrows(dst) == nr);
ggml_vec_cpy_f32(nc,
(float *) ((char *) dst->data + i*dst->nb[1]),
(float *) ((char *) src0->data + r*src0->nb[1]));
// TODO: multi-thread
for (int64_t i12 = 0; i12 < ne12; ++i12) {
for (int64_t i11 = 0; i11 < ne11; ++i11) {
for (int64_t i10 = 0; i10 < ne10; ++i10) {
const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
ggml_vec_cpy_f32(nc,
(float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3),
(float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03));
}
}
}
}
@ -14037,11 +14081,11 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
} break;
case GGML_OP_MUL_MAT:
{
ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor);
ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor, 0, tensor->ne[1]);
} break;
case GGML_OP_MUL_MAT_ID:
{
ggml_compute_forward_mul_mat_id(params, tensor);
ggml_compute_forward_mul_mat_id(params, tensor->src[0], tensor->src[1], tensor);
} break;
case GGML_OP_OUT_PROD:
{

6
ggml.h
View File

@ -217,7 +217,7 @@
#define GGML_MAX_DIMS 4
#define GGML_MAX_PARAMS 2048
#define GGML_MAX_CONTEXTS 64
#define GGML_MAX_SRC 6
#define GGML_MAX_SRC 10
#define GGML_MAX_NAME 64
#define GGML_MAX_OP_PARAMS 64
#define GGML_DEFAULT_N_THREADS 4
@ -1051,7 +1051,8 @@ extern "C" {
// ggml_mul_mat_id(ctx, as, ids, id, b) ~= ggml_mul_mat(as[ids[id]], b)
GGML_API struct ggml_tensor * ggml_mul_mat_id(
struct ggml_context * ctx,
struct ggml_tensor * as[],
struct ggml_tensor * const as[],
int n_as,
struct ggml_tensor * ids,
int id,
struct ggml_tensor * b);
@ -1263,6 +1264,7 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * a);
// supports 3D: a->ne[2] == b->ne[1]
GGML_API struct ggml_tensor * ggml_get_rows(
struct ggml_context * ctx,
struct ggml_tensor * a,

16
gguf-py/gguf/constants.py
View File

@ -38,6 +38,8 @@ class Keys:
FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout"
EXPERT_COUNT = "{arch}.expert_count"
EXPERT_USED_COUNT = "{arch}.expert_used_count"
class Attention:
HEAD_COUNT = "{arch}.attention.head_count"
@ -111,10 +113,14 @@ class MODEL_TENSOR(IntEnum):
ATTN_NORM = auto()
ATTN_NORM_2 = auto()
ATTN_ROT_EMBD = auto()
FFN_GATE_INP = auto()
FFN_NORM = auto()
FFN_GATE = auto()
FFN_DOWN = auto()
FFN_UP = auto()
FFN_NORM = auto()
FFN_GATE_EXP = auto()
FFN_DOWN_EXP = auto()
FFN_UP_EXP = auto()
ATTN_Q_NORM = auto()
ATTN_K_NORM = auto()
@ -154,10 +160,14 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd",
MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm",
MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm",
MODEL_TENSOR.FFN_GATE_INP: "blk.{bid}.ffn_gate_inp",
MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm",
MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate",
MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate.{xid}",
MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down.{xid}",
MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up.{xid}",
}
MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@ -172,10 +182,14 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_ROT_EMBD,
MODEL_TENSOR.FFN_GATE_INP,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.FFN_GATE_EXP,
MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_UP_EXP,
],
MODEL_ARCH.GPTNEOX: [
MODEL_TENSOR.TOKEN_EMBD,

6
gguf-py/gguf/gguf_writer.py
View File

@ -339,6 +339,12 @@ class GGUFWriter:
def add_clamp_kqv(self, value: float) -> None:
self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value)
def add_expert_count(self, count: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_COUNT.format(arch=self.arch), count)
def add_expert_used_count(self, count: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_USED_COUNT.format(arch=self.arch), count)
def add_layer_norm_eps(self, value: float) -> None:
self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value)

39
gguf-py/gguf/tensor_mapping.py
View File

@ -149,6 +149,11 @@ class TensorNameMap:
"model.layers.{bid}.ln2", # yi
),
MODEL_TENSOR.FFN_GATE_INP: (
"layers.{bid}.feed_forward.gate", # mixtral
"model.layers.{bid}.block_sparse_moe.gate", # mixtral
),
# Feed-forward up
MODEL_TENSOR.FFN_UP: (
"gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox
@ -164,11 +169,21 @@ class TensorNameMap:
"transformer.h.{bid}.mlp.w1", # qwen
),
MODEL_TENSOR.FFN_UP_EXP: (
"layers.{bid}.feed_forward.experts.{xid}.w3", # mixtral
"model.layers.{bid}.block_sparse_moe.experts.{xid}.w3", # mixtral
),
# Feed-forward gate
MODEL_TENSOR.FFN_GATE: (
"model.layers.{bid}.mlp.gate_proj", # llama-hf refact
"layers.{bid}.feed_forward.w1", # llama-pth
"transformer.h.{bid}.mlp.w2", # qwen
"model.layers.{bid}.mlp.gate_proj", # llama-hf refact
"layers.{bid}.feed_forward.w1", # llama-pth
"transformer.h.{bid}.mlp.w2", # qwen
),
MODEL_TENSOR.FFN_GATE_EXP: (
"layers.{bid}.feed_forward.experts.{xid}.w1", # mixtral
"model.layers.{bid}.block_sparse_moe.experts.{xid}.w1", # mixtral
),
# Feed-forward down
@ -185,6 +200,11 @@ class TensorNameMap:
"language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon
),
MODEL_TENSOR.FFN_DOWN_EXP: (
"layers.{bid}.feed_forward.experts.{xid}.w2", # mixtral
"model.layers.{bid}.block_sparse_moe.experts.{xid}.w2", # mixtral
),
MODEL_TENSOR.ATTN_Q_NORM: (
"language_model.encoder.layers.{bid}.self_attention.q_layernorm",
),
@ -213,11 +233,14 @@ class TensorNameMap:
for tensor, keys in self.block_mappings_cfg.items():
if tensor not in MODEL_TENSORS[arch]:
continue
tensor_name = TENSOR_NAMES[tensor].format(bid = bid)
self.mapping[tensor_name] = (tensor, tensor_name)
for key in keys:
key = key.format(bid = bid)
self.mapping[key] = (tensor, tensor_name)
# TODO: make this configurable
n_experts = 8
for xid in range(n_experts):
tensor_name = TENSOR_NAMES[tensor].format(bid = bid, xid = xid)
self.mapping[tensor_name] = (tensor, tensor_name)
for key in keys:
key = key.format(bid = bid, xid = xid)
self.mapping[key] = (tensor, tensor_name)
def get_type_and_name(self, key: str, try_suffixes: Sequence[str] = ()) -> tuple[MODEL_TENSOR, str] | None:
result = self.mapping.get(key)

2
gguf-py/pyproject.toml
View File

@ -1,6 +1,6 @@
[tool.poetry]
name = "gguf"
version = "0.6.0"
version = "0.7.0"
description = "Read and write ML models in GGUF for GGML"
authors = ["GGML <ggml@ggml.ai>"]
packages = [

202
llama.cpp
View File

@ -91,7 +91,8 @@
#define LLAMA_ATTRIBUTE_FORMAT(...)
#endif
#define LLAMA_MAX_NODES 8192
#define LLAMA_MAX_NODES 8192
#define LLAMA_MAX_EXPERTS 8
//
// logging
@ -231,6 +232,8 @@ enum llm_kv {
LLM_KV_FEED_FORWARD_LENGTH,
LLM_KV_USE_PARALLEL_RESIDUAL,
LLM_KV_TENSOR_DATA_LAYOUT,
LLM_KV_EXPERT_COUNT,
LLM_KV_EXPERT_USED_COUNT,
LLM_KV_ATTENTION_HEAD_COUNT,
LLM_KV_ATTENTION_HEAD_COUNT_KV,
@ -281,6 +284,8 @@ static std::map<llm_kv, std::string> LLM_KV_NAMES = {
{ LLM_KV_FEED_FORWARD_LENGTH, "%s.feed_forward_length" },
{ LLM_KV_USE_PARALLEL_RESIDUAL, "%s.use_parallel_residual" },
{ LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" },
{ LLM_KV_EXPERT_COUNT, "%s.expert_count" },
{ LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" },
{ LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" },
{ LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" },
@ -338,10 +343,14 @@ enum llm_tensor {
LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_ATTN_NORM_2,
LLM_TENSOR_ATTN_ROT_EMBD,
LLM_TENSOR_FFN_GATE_INP,
LLM_TENSOR_FFN_NORM,
LLM_TENSOR_FFN_GATE,
LLM_TENSOR_FFN_DOWN,
LLM_TENSOR_FFN_UP,
LLM_TENSOR_FFN_NORM,
LLM_TENSOR_FFN_DOWN_EXP,
LLM_TENSOR_FFN_GATE_EXP,
LLM_TENSOR_FFN_UP_EXP,
LLM_TENSOR_ATTN_Q_NORM,
LLM_TENSOR_ATTN_K_NORM,
};
@ -360,10 +369,14 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" },
{ LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
{ LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
},
},
{
@ -585,6 +598,10 @@ struct LLM_TN {
std::string operator()(llm_tensor tensor, const std::string & suffix, int bid) const {
return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid) + "." + suffix;
}
std::string operator()(llm_tensor tensor, const std::string & suffix, int bid, int xid) const {
return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid, xid) + "." + suffix;
}
};
//
@ -1164,6 +1181,8 @@ struct llama_hparams {
uint32_t n_layer;
uint32_t n_rot;
uint32_t n_ff;
uint32_t n_expert = 0;
uint32_t n_expert_used = 0;
float f_norm_eps;
float f_norm_rms_eps;
@ -1178,15 +1197,18 @@ struct llama_hparams {
float f_max_alibi_bias;
bool operator!=(const llama_hparams & other) const {
if (this->vocab_only != other.vocab_only) return true;
if (this->n_vocab != other.n_vocab) return true;
if (this->n_ctx_train != other.n_ctx_train) return true;
if (this->n_embd != other.n_embd) return true;
if (this->n_head != other.n_head) return true;
if (this->n_head_kv != other.n_head_kv) return true;
if (this->n_layer != other.n_layer) return true;
if (this->n_rot != other.n_rot) return true;
if (this->n_ff != other.n_ff) return true;
if (this->vocab_only != other.vocab_only) return true;
if (this->n_vocab != other.n_vocab) return true;
if (this->n_ctx_train != other.n_ctx_train) return true;
if (this->n_embd != other.n_embd) return true;
if (this->n_head != other.n_head) return true;
if (this->n_head_kv != other.n_head_kv) return true;
if (this->n_layer != other.n_layer) return true;
if (this->n_rot != other.n_rot) return true;
if (this->n_ff != other.n_ff) return true;
if (this->n_expert != other.n_expert) return true;
if (this->n_expert_used != other.n_expert_used) return true;
if (this->rope_finetuned != other.rope_finetuned) return true;
if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true;
@ -1268,6 +1290,12 @@ struct llama_layer {
struct ggml_tensor * ffn_down; // w2
struct ggml_tensor * ffn_up; // w3
// ff MoE
struct ggml_tensor * ffn_gate_inp;
struct ggml_tensor * ffn_gate_exp[LLAMA_MAX_EXPERTS];
struct ggml_tensor * ffn_down_exp[LLAMA_MAX_EXPERTS];
struct ggml_tensor * ffn_up_exp [LLAMA_MAX_EXPERTS];
// ff bias
struct ggml_tensor * ffn_down_b; // b2
struct ggml_tensor * ffn_up_b; // b3
@ -2440,6 +2468,16 @@ static void llm_load_hparams(
ml.get_key (LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff);
ml.get_key (LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head);
ml.get_key (LLM_KV_BLOCK_COUNT, hparams.n_layer);
ml.get_key (LLM_KV_EXPERT_COUNT, hparams.n_expert, false);
ml.get_key (LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);
GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert);
if (hparams.n_expert > 0) {
GGML_ASSERT(hparams.n_expert_used > 0);
} else {
GGML_ASSERT(hparams.n_expert_used == 0);
}
// n_head_kv is optional, default to n_head
hparams.n_head_kv = hparams.n_head;
@ -2871,6 +2909,8 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
LLAMA_LOG_INFO("%s: f_clamp_kqv = %.1e\n", __func__, hparams.f_clamp_kqv);
LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n", __func__, hparams.f_max_alibi_bias);
LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff);
LLAMA_LOG_INFO("%s: n_expert = %u\n", __func__, hparams.n_expert);
LLAMA_LOG_INFO("%s: n_expert_used = %u\n", __func__, hparams.n_expert_used);
LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type.c_str());
LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train);
LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train);
@ -3025,9 +3065,26 @@ static void llm_load_tensors(
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
layer.ffn_gate_inp = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd}, backend, false);
if (layer.ffn_gate_inp == nullptr) {
GGML_ASSERT(hparams.n_expert == 0);
GGML_ASSERT(hparams.n_expert_used == 0);
layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
} else {
GGML_ASSERT(hparams.n_expert > 0);
GGML_ASSERT(hparams.n_expert_used > 0);
// MoE branch
for (uint32_t x = 0; x < hparams.n_expert; ++x) {
layer.ffn_gate_exp[x] = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), {n_embd, n_ff}, backend_split);
layer.ffn_down_exp[x] = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd}, backend_split);
layer.ffn_up_exp[x] = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), {n_embd, n_ff}, backend_split);
}
}
if (backend == GGML_BACKEND_GPU) {
vram_weights +=
@ -3037,8 +3094,18 @@ static void llm_load_tensors(
(layer.bk ? ggml_nbytes(layer.bk) : 0) +
(layer.bv ? ggml_nbytes(layer.bv) : 0) +
(layer.bo ? ggml_nbytes(layer.bo) : 0) +
ggml_nbytes(layer.ffn_norm) + ggml_nbytes(layer.ffn_gate) +
ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
ggml_nbytes(layer.ffn_norm);
if (layer.ffn_gate_inp == nullptr) {
vram_weights +=
ggml_nbytes(layer.ffn_gate) + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
} else {
vram_weights += ggml_nbytes(layer.ffn_gate_inp);
for (uint32_t x = 0; x < hparams.n_expert; ++x) {
vram_weights +=
ggml_nbytes(layer.ffn_gate_exp[x]) + ggml_nbytes(layer.ffn_down_exp[x]) + ggml_nbytes(layer.ffn_up_exp[x]);
}
}
}
}
} break;
@ -4019,6 +4086,8 @@ struct llm_build_context {
const int64_t n_head_kv;
const int64_t n_embd_head;
const int64_t n_embd_gqa;
const int64_t n_expert;
const int64_t n_expert_used;
const float freq_base;
const float freq_scale;
@ -4060,6 +4129,8 @@ struct llm_build_context {
n_head_kv (hparams.n_head_kv),
n_embd_head (hparams.n_embd_head()),
n_embd_gqa (hparams.n_embd_gqa()),
n_expert (hparams.n_expert),
n_expert_used (hparams.n_expert_used),
freq_base (cparams.rope_freq_base),
freq_scale (cparams.rope_freq_scale),
ext_factor (cparams.yarn_ext_factor),
@ -4184,7 +4255,7 @@ struct llm_build_context {
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
{
if (model.layers[il].ffn_gate_inp == nullptr) {
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
@ -4196,6 +4267,69 @@ struct llm_build_context {
model.layers[il].ffn_down, NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
cb(cur, "ffn_out", il);
} else {
// MoE branch
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts]
cb(logits, "ffn_moe_logits", il);
ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts]
cb(probs, "ffn_moe_probs", il);
// select experts
ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok]
cb(selected_experts->src[0], "ffn_moe_argsort", il);
ggml_tensor * weights = ggml_get_rows(ctx0,
ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts);
cb(weights, "ffn_moe_weights", il);
weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok]
ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights);
cb(weights_sum, "ffn_moe_weights_sum", il);
weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok]
cb(weights, "ffn_moe_weights_norm", il);
// compute expert outputs
ggml_tensor * moe_out = nullptr;
for (int i = 0; i < n_expert_used; ++i) {
ggml_tensor * cur_expert;
ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exp, n_expert, selected_experts, i, cur);
cb(cur_up, "ffn_moe_up", il);
ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exp, n_expert, selected_experts, i, cur);
cb(cur_gate, "ffn_moe_gate", il);
cur_gate = ggml_silu(ctx0, cur_gate);
cb(cur_gate, "ffn_moe_silu", il);
cur_expert = ggml_mul(ctx0, cur_up, cur_gate); // [n_tokens, n_embd]
cb(cur_expert, "ffn_moe_gate_par", il);
cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exp, n_expert, selected_experts, i, cur_expert); // [n_tokens, n_embd]
cb(cur_expert, "ffn_moe_down", il);
cur_expert = ggml_mul(ctx0, cur_expert,
ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
cb(cur_expert, "ffn_moe_weighted", il);
if (i == 0) {
moe_out = cur_expert;
} else {
moe_out = ggml_add(ctx0, moe_out, cur_expert);
cb(moe_out, "ffn_moe_out", il);
}
}
cur = moe_out;
}
cur = ggml_add(ctx0, cur, ffn_inp);
@ -5450,6 +5584,20 @@ static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map
{ "ffn_relu", OFFLOAD_FUNC },
{ "ffn_sqr(relu)", OFFLOAD_FUNC },
{ "ffn_moe_logits", OFFLOAD_FUNC },
{ "ffn_moe_probs", OFFLOAD_FUNC },
{ "ffn_moe_argsort", OFFLOAD_FUNC },
{ "ffn_moe_weights", OFFLOAD_FUNC },
{ "ffn_moe_weights_sum", OFFLOAD_FUNC },
{ "ffn_moe_weights_norm", OFFLOAD_FUNC },
{ "ffn_moe_weighted", OFFLOAD_FUNC },
{ "ffn_moe_up", OFFLOAD_FUNC },
{ "ffn_moe_gate", OFFLOAD_FUNC },
{ "ffn_moe_silu", OFFLOAD_FUNC },
{ "ffn_moe_gate_par", OFFLOAD_FUNC },
{ "ffn_moe_down", OFFLOAD_FUNC },
{ "ffn_moe_out", OFFLOAD_FUNC },
{ "l_out", OFFLOAD_FUNC },
{ "result_norm", OFFLOAD_FUNC_EMB },
@ -8067,11 +8215,9 @@ static void llama_convert_tensor_internal(
workers.clear();
}
static ggml_type get_k_quant_type(
quantize_state_internal & qs,
ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype
) {
static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
const std::string name = ggml_get_name(tensor);
// TODO: avoid hardcoded tensor names - use the TN_* constants
const llm_arch arch = qs.model.arch;
const auto tn = LLM_TN(arch);
@ -8105,7 +8251,18 @@ static ggml_type get_k_quant_type(
// nearly negligible increase in model size by quantizing this tensor with more bits:
if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K;
}
if (qs.model.hparams.n_expert == 8) {
// for the 8-expert model, bumping this to Q8_0 trades just ~128MB
// TODO: explore better strategies
new_type = GGML_TYPE_Q8_0;
}
++qs.i_attention_wv;
} else if (name.find("attn_k.weight") != std::string::npos) {
if (qs.model.hparams.n_expert == 8) {
// for the 8-expert model, bumping this to Q8_0 trades just ~128MB
// TODO: explore better strategies
new_type = GGML_TYPE_Q8_0;
}
} else if (name.find("ffn_down.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
@ -8318,6 +8475,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
quantize &= params->quantize_output_tensor || name != "output.weight";
quantize &= !params->only_copy;
// do not quantize expert gating tensors
quantize &= name.find("ffn_gate_inp.weight") == std::string::npos;
enum ggml_type new_type;
void * new_data;
size_t new_size;

277
tests/test-backend-ops.cpp
View File

@ -20,8 +20,6 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
size_t size = ggml_nelements(tensor);
std::vector<float> data(size);
std::random_device rd;
#if 0
std::default_random_engine generator(rd());
std::uniform_real_distribution<float> distribution(min, max);
@ -31,6 +29,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
}
#endif
auto init_thread = [&](size_t start, size_t end) {
std::random_device rd;
std::default_random_engine generator(rd());
std::uniform_real_distribution<float> distribution(min, max);
@ -51,7 +50,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
t.join();
}
if (tensor->type == GGML_TYPE_F32) {
if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) {
ggml_backend_tensor_set(tensor, data.data(), 0, size * sizeof(float));
} else if (ggml_is_quantized(tensor->type) || tensor->type == GGML_TYPE_F16) {
GGML_ASSERT(size % ggml_blck_size(tensor->type) == 0);
@ -71,23 +70,28 @@ static std::vector<float> tensor_to_float(const ggml_tensor * t) {
std::vector<uint8_t> buf(ggml_nbytes(t));
ggml_backend_tensor_get(t, buf.data(), 0, ggml_nbytes(t));
ggml_type_traits_t tt = ggml_internal_get_type_traits(t->type);
size_t bs = ggml_blck_size(t->type);
// access elements by index to avoid gaps in views
for (int64_t i3 = 0; i3 < t->ne[3]; i3++) {
for (int64_t i2 = 0; i2 < t->ne[2]; i2++) {
for (int64_t i1 = 0; i1 < t->ne[1]; i1++) {
for (int64_t i0 = 0; i0 < t->ne[0]; i0++) {
size_t i = i3*t->nb[3] + i2*t->nb[2] + i1*t->nb[1] + i0*t->nb[0];
float v;
for (int64_t i0 = 0; i0 < t->ne[0]; i0 += bs) {
size_t i = i3*t->nb[3] + i2*t->nb[2] + i1*t->nb[1] + i0/bs*t->nb[0];
if (t->type == GGML_TYPE_F16) {
v = (float) ggml_fp16_to_fp32(*(ggml_fp16_t*)&buf[i]);
tv.push_back(ggml_fp16_to_fp32(*(ggml_fp16_t*)&buf[i]));
} else if (t->type == GGML_TYPE_F32) {
v = *(float *) &buf[i];
tv.push_back(*(float *) &buf[i]);
} else if (t->type == GGML_TYPE_I32) {
v = *(int32_t *) &buf[i];
tv.push_back((float)*(int32_t *) &buf[i]);
} else if (ggml_is_quantized(t->type)) {
std::vector<float> vq(ggml_blck_size(t->type));
tt.to_float(&buf[i], vq.data(), ggml_blck_size(t->type));
tv.insert(tv.end(), vq.begin(), vq.end());
} else {
GGML_ASSERT(false);
}
tv.push_back(v);
}
}
}
@ -233,6 +237,10 @@ static bool ggml_is_view_op(enum ggml_op op) {
struct test_case {
virtual ~test_case() {}
virtual std::string op_desc(ggml_tensor * t) {
return ggml_op_desc(t);
}
virtual std::string vars() {
return "";
}
@ -240,7 +248,7 @@ struct test_case {
virtual ggml_tensor * build_graph(ggml_context * ctx) = 0;
virtual double max_nmse_err() {
return 1e-6;
return 1e-7;
}
virtual void initialize_tensors(ggml_context * ctx) {
@ -270,13 +278,13 @@ struct test_case {
ggml_tensor * out = build_graph(ctx);
if (op_name != nullptr && strcmp(ggml_op_desc(out), op_name) != 0) {
//printf(" %s: skipping\n", ggml_op_desc(out));
if (op_name != nullptr && op_desc(out) != op_name) {
//printf(" %s: skipping\n", op_desc(out).c_str());
ggml_free(ctx);
return true;
}
printf(" %s(%s): ", ggml_op_desc(out), vars().c_str());
printf(" %s(%s): ", op_desc(out).c_str(), vars().c_str());
fflush(stdout);
// check if backends support op
@ -317,7 +325,7 @@ struct test_case {
for (size_t i = 0; i < f1.size(); i++) {
// check for nans
if (std::isnan(f1[i]) || std::isnan(f2[i])) {
printf("NaN at index %zu ", i);
printf("[%s] NaN at index %zu (%f %f) ", ggml_op_desc(t1), i, f1[i], f2[i]);
ud->ok = false;
return true;
}
@ -325,12 +333,12 @@ struct test_case {
if (isinf_or_max(f1[i]) || isinf_or_max(f2[i])) {
if (isinf_or_max(f1[i]) && isinf_or_max(f2[i])) {
if (std::signbit(f1[i]) != std::signbit(f2[i])) {
printf("inf sign mismatch: %f %f ", f1[i], f2[i]);
printf("[%s] inf sign mismatch: %f %f ", ggml_op_desc(t1), f1[i], f2[i]);
ud->ok = false;
return true;
}
} else {
printf("inf mismatch: %f %f ", f1[i], f2[i]);
printf("[%s] inf mismatch: %f %f ", ggml_op_desc(t1), f1[i], f2[i]);
ud->ok = false;
return true;
}
@ -339,10 +347,16 @@ struct test_case {
double err = nmse(f1.data(), f2.data(), f1.size());
if (err > ud->max_err) {
printf("NMSE = %f ", err);
printf("[%s] NMSE = %f ", ggml_op_desc(t1), err);
//for (int i = 0; i < f1.size(); i++) {
// printf("(%f, %f) ", f1[i], f2[i]);
//}
//printf("\n");
ud->ok = false;
}
return true;
GGML_UNUSED(index);
};
ggml_backend_compare_graph_backend(backend1, backend2, gf, callback, &ud);
@ -372,13 +386,13 @@ struct test_case {
ggml_tensor * out = build_graph(ctx);
if (op_name != nullptr && strcmp(ggml_op_desc(out), op_name) != 0) {
//printf(" %s: skipping\n", ggml_op_desc(out));
if (op_name != nullptr && op_desc(out) != op_name) {
//printf(" %s: skipping\n", op_desc(out).c_str());
ggml_free(ctx);
return true;
}
int len = printf(" %s(%s): ", ggml_op_desc(out), vars().c_str());
int len = printf(" %s(%s): ", op_desc(out).c_str(), vars().c_str());
fflush(stdout);
// check if backends support op
@ -430,8 +444,9 @@ struct test_case {
return size;
};
for (int i = 0; i < gf->n_nodes; i++) {
if (ggml_is_view_op(gf->nodes[i]->op) || gf->nodes[i] == out)
if (ggml_is_view_op(gf->nodes[i]->op) || gf->nodes[i] == out) {
continue;
}
mem += tensor_op_size(gf->nodes[i]);
}
@ -486,17 +501,22 @@ struct test_get_rows : public test_case {
const int n; // cols
const int m; // rows
const int r; // rows to get
const int b; // batch size
const bool v; // view (non-contiguous src1)
std::string vars() override {
return VARS_TO_STR4(type, n, m, r);
return VARS_TO_STR6(type, n, m, r, b, v);
}
test_get_rows(ggml_type type = GGML_TYPE_F32, int n = 10, int m = 5, int r = 3)
: type(type), n(n), m(m), r(r) {}
test_get_rows(ggml_type type = GGML_TYPE_F32, int n = 10, int m = 5, int r = 3, int b = 1, bool v = false)
: type(type), n(n), m(m), r(r), b(b), v(v) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * in = ggml_new_tensor_2d(ctx, type, n, m);
ggml_tensor * rows = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, r);
ggml_tensor * in = ggml_new_tensor_3d(ctx, type, n, m, b);
ggml_tensor * rows = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, r, b);
if (v) {
rows = ggml_view_2d(ctx, rows, r/2, b, rows->nb[1], 0);
}
ggml_tensor * out = ggml_get_rows(ctx, in, rows);
return out;
}
@ -504,12 +524,13 @@ struct test_get_rows : public test_case {
void initialize_tensors(ggml_context * ctx) override {
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (t->type == GGML_TYPE_I32) {
if (ggml_is_view_op(t->op)) { continue; }
// rows
std::vector<int> data(r);
for (int i = 0; i < r; i++) {
std::vector<int> data(r*b);
for (int i = 0; i < r*b; i++) {
data[i] = rand() % m;
}
ggml_backend_tensor_set(t, data.data(), 0, r * sizeof(int));
ggml_backend_tensor_set(t, data.data(), 0, r * b * sizeof(int));
} else {
init_tensor_uniform(t);
}
@ -770,11 +791,10 @@ struct test_mul_mat_id : public test_case {
const int64_t m;
const int64_t n;
const int64_t k;
const std::array<int64_t, 2> bs; // dims 3 and 4
const std::array<int64_t, 2> nr; // repeat in dims 3 and 4
const bool v; // view (non-contiguous ids)
std::string vars() override {
return VARS_TO_STR9(type_a, type_b, n_mats, id, m, n, k, bs, nr);
return VARS_TO_STR8(type_a, type_b, n_mats, id, m, n, k, v);
}
double max_nmse_err() override {
@ -782,7 +802,7 @@ struct test_mul_mat_id : public test_case {
}
size_t op_size(ggml_tensor * t) override {
size_t a = ggml_nbytes(t->src[2]) * n * nr[0] * nr[1];
size_t a = ggml_nbytes(t->src[2]) * n;
size_t b = ggml_nbytes(t->src[1]) * m;
size_t c = ggml_nbytes(t);
return a + b + c;
@ -792,35 +812,41 @@ struct test_mul_mat_id : public test_case {
test_mul_mat_id(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
int n_mats = 2, int id = 0,
int64_t m = 32, int64_t n = 32, int64_t k = 32,
std::array<int64_t, 2> bs = {10, 10},
std::array<int64_t, 2> nr = {2, 2})
int64_t m = 32, int64_t n = 32, int64_t k = 32, bool v = false)
: type_a(type_a), type_b(type_b), n_mats(n_mats), id(id),
m(m), n(n), k(k), bs(bs), nr(nr) {}
m(m), n(n), k(k), v(v) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
// C^T = A * B^T: (k, m) * (k, n) => (m, n)
std::vector<ggml_tensor *> mats;
for (int i = 0; i < n_mats; i++) {
ggml_tensor * a = ggml_new_tensor_4d(ctx, type_a, k, m, bs[0], bs[1]);
ggml_tensor * a = ggml_new_tensor_2d(ctx, type_a, k, m);
mats.push_back(a);
}
ggml_tensor * ids = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_mats);
ggml_tensor * b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]);
ggml_tensor * out = ggml_mul_mat_id(ctx, mats.data(), ids, id, b);
ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_mats, n);
if (v) {
ids = ggml_view_2d(ctx, ids, n_mats/2, ids->ne[1], ids->nb[1], 0);
}
ggml_tensor * b = ggml_new_tensor_2d(ctx, type_b, k, n);
ggml_tensor * out = ggml_mul_mat_id(ctx, mats.data(), n_mats, ids, v ? id/2 : id, b);
return out;
}
void initialize_tensors(ggml_context * ctx) override {
std::random_device rd;
std::default_random_engine rng(rd());
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (t->type == GGML_TYPE_I32) {
if (ggml_is_view_op(t->op)) { continue; }
// ids
std::vector<int> data(n_mats);
for (int i = 0; i < n_mats; i++) {
data[i] = i;
for (int64_t r = 0; r < ggml_nrows(t); r++) {
std::vector<int32_t> data(t->ne[0]);
for (int i = 0; i < t->ne[0]; i++) {
data[i] = i % n_mats;
}
std::shuffle(data.begin(), data.end(), rng);
ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(int32_t));
}
std::shuffle(data.begin(), data.end(), std::default_random_engine(std::random_device()()));
ggml_backend_tensor_set(t, data.data(), 0, n_mats * sizeof(int));
} else {
init_tensor_uniform(t);
}
@ -1109,6 +1135,90 @@ struct test_sum_rows : public test_case {
}
};
// Mixtral MOE
struct test_moe : public test_case {
const int n_experts;
const int n_experts_per_tok;
const int n_tokens;
const int n_embd;
const int n_ff;
std::string op_desc(ggml_tensor * t) override {
return "MOE";
GGML_UNUSED(t);
}
std::string vars() override {
return VARS_TO_STR5(n_experts, n_experts_per_tok, n_tokens, n_embd, n_ff);
}
test_moe(int n_experts = 8, int n_experts_per_tok = 2, int n_tokens = 1, int n_embd = 4096, int n_ff = 14336)
: n_experts(n_experts), n_experts_per_tok(n_experts_per_tok), n_tokens(n_tokens), n_embd(n_embd), n_ff(n_ff) {
}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * ffn_gate_inp = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_experts);
std::vector<ggml_tensor *> ffn_up_exp(n_experts);
std::vector<ggml_tensor *> ffn_gate_exp(n_experts);
std::vector<ggml_tensor *> ffn_down_exp(n_experts);
for (int i = 0; i < n_experts; ++i) {
ffn_up_exp[i] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
ffn_gate_exp[i] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
ffn_down_exp[i] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd);
}
ggml_tensor * cur = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_tokens);
ggml_tensor * logits = ggml_mul_mat(ctx, ffn_gate_inp, cur);
ggml_tensor * probs = ggml_soft_max_ext(ctx, logits, nullptr, 1.0f/sqrtf(n_embd));
// select experts
ggml_tensor * selected_experts = ggml_top_k(ctx, probs, n_experts_per_tok);
ggml_tensor * weights = ggml_get_rows(ctx,
ggml_reshape_3d(ctx, probs, 1, n_experts, n_tokens), selected_experts);
weights = ggml_reshape_2d(ctx, weights, n_experts_per_tok, n_tokens);
ggml_tensor * weights_sum = ggml_sum_rows(ctx, weights);
weights = ggml_div(ctx, weights, weights_sum);
// compute expert outputs
ggml_tensor * moe_out = nullptr;
for (int i = 0; i < n_experts_per_tok; ++i) {
ggml_tensor * cur_expert;
ggml_tensor * cur_up = ggml_mul_mat_id(ctx, ffn_up_exp.data(), n_experts, selected_experts, i, cur);
ggml_tensor * cur_gate = ggml_mul_mat_id(ctx, ffn_gate_exp.data(), n_experts, selected_experts, i, cur);
cur_gate = ggml_silu(ctx, cur_gate);
cur_expert = ggml_mul(ctx, cur_up, cur_gate);
cur_expert = ggml_mul_mat_id(ctx, ffn_down_exp.data(), n_experts, selected_experts, i, cur_expert);
cur_expert = ggml_mul(ctx, cur_expert,
ggml_view_2d(ctx, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
if (i == 0) {
moe_out = cur_expert;
} else {
moe_out = ggml_add(ctx, moe_out, cur_expert);
}
}
cur = moe_out;
return cur;
}
};
enum test_mode {
MODE_TEST,
MODE_PERF,
@ -1117,14 +1227,28 @@ enum test_mode {
static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op_name) {
std::vector<std::unique_ptr<test_case>> test_cases;
const ggml_type all_types[] = {
GGML_TYPE_F32, GGML_TYPE_F16,
GGML_TYPE_Q4_0, GGML_TYPE_Q4_1,
GGML_TYPE_Q5_0, GGML_TYPE_Q5_1,
GGML_TYPE_Q8_0,
GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
GGML_TYPE_Q4_K, GGML_TYPE_Q5_K,
GGML_TYPE_Q6_K
};
// unary ops
for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) {
test_cases.emplace_back(new test_unary((ggml_unary_op) op));
}
for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
test_cases.emplace_back(new test_get_rows(type, 10, 5, 3));
test_cases.emplace_back(new test_get_rows(type, 16, 5, 3));
test_cases.emplace_back(new test_get_rows(GGML_TYPE_F32, 1, 8, 2, 1, false));
for (ggml_type type : all_types) {
for (int b : {1, 7}) {
for (bool v : {false, true}) {
test_cases.emplace_back(new test_get_rows(type, 256, 5, 4, b, v));
}
}
}
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 1}));
@ -1134,7 +1258,11 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 2}));
test_cases.emplace_back(new test_dup());
test_cases.emplace_back(new test_cpy());
for (ggml_type type : all_types) {
test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, type, {256, 10, 10, 1}));
}
test_cases.emplace_back(new test_cont());
auto add_test_bin_bcast = [&](ggml_type type, std::array<int64_t, 4> ne, std::array<int, 4> nr) {
@ -1144,6 +1272,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
};
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 8, 1}, {1, 1, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1, 1}, {32, 1, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 320, 320}, {1, 1, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 1, 1}, {1, 1, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {16, 10, 10, 1}, {1, 1, 1, 1});
@ -1170,8 +1299,8 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 640, 1}, {32, 32, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {5120, 1, 1, 1}, {1, 256, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {640, 1, 1, 1}, {1, 1, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {3, 3, 2560, 1280}, {1, 1, 1, 1});
add_test_bin_bcast(GGML_TYPE_F32, {3, 3, 2560, 1280}, {2, 1, 1, 1});
//add_test_bin_bcast(GGML_TYPE_F32, {3, 3, 2560, 1280}, {1, 1, 1, 1});
//add_test_bin_bcast(GGML_TYPE_F32, {3, 3, 2560, 1280}, {2, 1, 1, 1});
test_cases.emplace_back(new test_scale());
@ -1180,16 +1309,6 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
test_cases.emplace_back(new test_rms_norm(GGML_TYPE_F32, {64, 10, 10, 10}, eps));
}
const ggml_type all_types[] = {
GGML_TYPE_F32, GGML_TYPE_F16,
GGML_TYPE_Q4_0, GGML_TYPE_Q4_1,
GGML_TYPE_Q5_0, GGML_TYPE_Q5_1,
GGML_TYPE_Q8_0,
GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
GGML_TYPE_Q4_K, GGML_TYPE_Q5_K,
GGML_TYPE_Q6_K
};
for (ggml_type type_a : all_types) {
for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) {
// FIXME: CPU crashes on f16xf16
@ -1213,9 +1332,11 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
for (ggml_type type_a : all_types) {
for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) {
for (int n_mats : {1, 2, 4}) {
for (int n_mats : {2, 4, 8}) {
for (int id = 0; id < n_mats; id++) {
test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, id, 16, 16, 256, {1, 1}, {1, 1}));
for (bool v : {false, true}) {
test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, id, 16, 16, 256, v));
}
}
}
}
@ -1247,10 +1368,18 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
test_cases.emplace_back(new test_concat());
for (ggml_sort_order order : {GGML_SORT_ASC, GGML_SORT_DESC}) {
test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order));
test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {16, 10, 10, 10}, order));
}
test_cases.emplace_back(new test_sum_rows());
test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, {10, 10, 10, 10}));
test_cases.emplace_back(new test_sum_rows(GGML_TYPE_F32, {2, 1, 1, 1}));
#if !defined(__SANITIZE_THREAD__)
// FIXME: these tests use too much memory with thread sanitizer
test_cases.emplace_back(new test_moe(8, 2, 1, 4096, 14336));
//test_cases.emplace_back(new test_moe(8, 2, 8, 4096, 14336));
#endif
// run tests
if (mode == MODE_TEST) {
@ -1267,14 +1396,17 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
ggml_backend_free(backend_cpu);
return n_ok == test_cases.size();
} else if (mode == MODE_PERF) {
}
if (mode == MODE_PERF) {
for (auto & test : test_cases) {
test->eval_perf(backend, op_name);
}
return true;
} else {
GGML_ASSERT(false);
}
GGML_ASSERT(false);
return false;
}
static void usage(char ** argv) {
@ -1347,11 +1479,12 @@ int main(int argc, char ** argv) {
}
printf("%zu/%zu backends passed\n", n_ok, ggml_backend_reg_get_count());
if (n_ok != ggml_backend_reg_get_count()) {
printf("\033[1;31mFAIL\033[0m\n");
return 1;
} else {
printf("\033[1;32mOK\033[0m\n");
return 0;
}
printf("\033[1;32mOK\033[0m\n");
return 0;
}