vbatts/llama.cpp
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some modification after review

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This commit is contained in:
shanshan shen 2024-11-25 08:05:54 +00:00
parent d3c57c1eed
commit df68663a63
2 changed files with 22 additions and 207 deletions
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197
ggml/src/ggml-cann/aclnn_ops.cpp
View file

@ -2531,47 +2531,6 @@ static void aclnn_mat_mul_3d(ggml_backend_cann_context& ctx, aclTensor* acl_inpu
* multiplication will be stored.
*/
static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx,
ggml_tensor* dst) {
ggml_tensor* weight = dst->src[0]; // weight
ggml_tensor* input = dst->src[1]; // input
// when weight ne2 or ne3 is 1, aclnnMatmulGetWorkspaceSize will auto
// broadcast, when weight ne2 or ne3 is not 1, weight need repeat.
BCAST_MUL_MAT_SHAPE(input, weight, dst);
// transpose weight: [1,2,3,4] -> [1,2,4,3]
int64_t transpose_ne[] = {bcast_weight_ne[1], bcast_weight_ne[0],
bcast_weight_ne[2], bcast_weight_ne[3],
bcast_weight_ne[4], bcast_weight_ne[5]};
size_t transpose_nb[] = {bcast_weight_nb[1], bcast_weight_nb[0],
bcast_weight_nb[2], bcast_weight_nb[3],
bcast_weight_nb[4], bcast_weight_nb[5]};
aclTensor* acl_weight_tensor =
ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, bcast_dims);
aclTensor* acl_input_tensor =
ggml_cann_create_tensor(input, BCAST_MUL_MAT_PARAM(input));
aclTensor* acl_dst = ggml_cann_create_tensor(dst, BCAST_MUL_MAT_PARAM(dst));
aclnn_mat_mul(ctx, acl_input_tensor, acl_weight_tensor, acl_dst);
ACL_CHECK(aclDestroyTensor(acl_weight_tensor));
ACL_CHECK(aclDestroyTensor(acl_input_tensor));
ACL_CHECK(aclDestroyTensor(acl_dst));
}
/**
* @brief Performs matrix multiplication with floating-point precision on
* tensors using the CANN backend.
*
* This function performs matrix multiplication of the input tensor and the
* weight tensor, handling broadcasting and transposing as needed, and stores
* the result in the destination tensor `dst`.
*
* @param ctx The context for the CANN backend operations.
* @param dst The destination tensor where the result of the matrix
* multiplication will be stored.
*/
static void ggml_cann_mat_mul_fp2(ggml_backend_cann_context& ctx,
ggml_tensor* dst) {
ggml_tensor* weight = dst->src[0]; // weight
ggml_tensor* input = dst->src[1]; // input
@ -2637,158 +2596,6 @@ static void ggml_cann_mat_mul_fp2(ggml_backend_cann_context& ctx,
* multiplication will be stored.
*/
static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx,
ggml_tensor* dst,
const enum ggml_type type) {
ggml_tensor* src0 = dst->src[0]; // weight
ggml_tensor* src1 = dst->src[1]; // input
// The shape of the weight is NCHW. Matrix multiplication uses HW dims. HC
// is regarded as batch. weight need transpose.
int64_t weight_ne[] = {src0->ne[1], src0->ne[0]};
float weight_elem_size;
if (type == GGML_TYPE_Q4_0) {
weight_elem_size = float(sizeof(uint8_t)) / 2;
}
else if (type == GGML_TYPE_Q8_0) {
weight_elem_size = float(sizeof(uint8_t));
}
else {
GGML_ABORT("Only support Q4_0 and Q8_0 MUL_MAT");
}
float weight_nb[] = {weight_elem_size * src0->ne[0], weight_elem_size};
// size of one matrix is element_size * height * width.
size_t weight_stride = weight_elem_size * src0->ne[0] * src0->ne[1];
size_t weight_size = weight_stride * src0->ne[2] * src0->ne[3];
// scale stored at the end of weight. Also need transpose.
GGML_ASSERT(QK4_0 == QK8_0);
int64_t scale_ne[] = {src0->ne[1], src0->ne[0] / QK8_0};
size_t scale_elem_size = sizeof(uint16_t);
size_t scale_nb[] = {src0->ne[0] / QK8_0 * scale_elem_size,
scale_elem_size};
size_t scale_stride = scale_elem_size * src0->ne[0] * src0->ne[1] / QK8_0;
char* scale_offset = (char*)src0->data + weight_size;
// input
void* input_buffer;
size_t input_elem_size = sizeof(uint16_t);
int64_t input_ne[] = {src1->ne[0], src1->ne[1]};
size_t input_nb[] = {input_elem_size, input_elem_size * src1->ne[0]};
size_t input_stride = input_elem_size * src1->ne[0] * src1->ne[1];
ggml_cann_pool_alloc input_alloctor(ctx.pool());
if (src1->type != GGML_TYPE_F16) {
aclTensor* acl_src1_tensor = ggml_cann_create_tensor(src1);
input_alloctor.alloc(ggml_nelements(src1) * input_elem_size);
input_buffer = input_alloctor.get();
int64_t* input_cast_ne = src1->ne;
size_t input_cast_nb[GGML_MAX_DIMS];
input_cast_nb[0] = sizeof(uint16_t);
for (int i = 1; i < GGML_MAX_DIMS; i++) {
input_cast_nb[i] = input_cast_nb[i - 1] * input_cast_ne[i - 1];
}
aclTensor* acl_input_tensor = ggml_cann_create_tensor(
input_buffer, ACL_FLOAT16, input_elem_size, input_cast_ne,
input_cast_nb, GGML_MAX_DIMS);
aclnn_cast(ctx, acl_src1_tensor, acl_input_tensor, ACL_FLOAT16);
ACL_CHECK(aclDestroyTensor(acl_input_tensor));
ACL_CHECK(aclDestroyTensor(acl_src1_tensor));
} else {
input_buffer = src1->data;
}
// output
size_t output_elem_size = sizeof(uint16_t);
int64_t output_ne[] = {dst->ne[0], dst->ne[1]};
size_t output_nb[] = {output_elem_size, output_elem_size * dst->ne[0]};
ggml_cann_pool_alloc output_alloctor(
ctx.pool(), ggml_nelements(dst) * output_elem_size);
void* output_buffer = output_alloctor.get();
size_t output_stride = output_elem_size * dst->ne[0] * dst->ne[1];
// aclnn
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
void* workspaceAddr = nullptr;
for (int64_t n1 = 0; n1 < src1->ne[3]; n1++) {
for (int64_t c1 = 0; c1 < src1->ne[2]; c1++) {
int64_t n0 = n1 / (src1->ne[3] / src0->ne[3]);
int64_t c0 = c1 / (src1->ne[2] / src0->ne[2]);
int64_t batch1 = n1 * src1->ne[2] + c1;
int64_t batch0 = n0 * src0->ne[2] + c0;
aclTensor* acl_input_tensor = ggml_cann_create_tensor(
(char*)input_buffer + batch1 * input_stride, ACL_FLOAT16,
input_elem_size, input_ne, input_nb, 2);
aclTensor* acl_weight_tensor = ggml_cann_create_tensor(
(char*)src0->data + batch0 * weight_stride,
ggml_cann_type_mapping(type), weight_elem_size, weight_ne,
weight_nb, 2);
aclTensor* acl_scale_tensor = ggml_cann_create_tensor(
scale_offset + batch0 * scale_stride, ACL_FLOAT16,
scale_elem_size, scale_ne, scale_nb, 2);
aclTensor* acl_output_tensor = ggml_cann_create_tensor(
(char*)output_buffer + batch1 * output_stride, ACL_FLOAT16,
output_elem_size, output_ne, output_nb, 2);
ACL_CHECK(aclnnWeightQuantBatchMatmulV2GetWorkspaceSize(
acl_input_tensor, acl_weight_tensor, acl_scale_tensor, nullptr,
nullptr, nullptr, nullptr, QK8_0, acl_output_tensor,
&workspaceSize, &executor));
if (workspaceSize > 0 && workspaceAddr == nullptr) {
ggml_cann_pool_alloc workspace_allocator(ctx.pool(),
workspaceSize);
workspaceAddr = workspace_allocator.get();
}
ACL_CHECK(aclnnWeightQuantBatchMatmulV2(
workspaceAddr, workspaceSize, executor, ctx.stream()));
ACL_CHECK(aclDestroyTensor(acl_input_tensor));
ACL_CHECK(aclDestroyTensor(acl_weight_tensor));
ACL_CHECK(aclDestroyTensor(acl_scale_tensor));
ACL_CHECK(aclDestroyTensor(acl_output_tensor));
}
}
// cast out
int64_t* output_cast_ne = dst->ne;
size_t output_cast_nb[GGML_MAX_DIMS];
output_cast_nb[0] = sizeof(uint16_t);
for (int i = 1; i < GGML_MAX_DIMS; i++) {
output_cast_nb[i] = output_cast_nb[i - 1] * output_cast_ne[i - 1];
}
aclTensor* acl_output_tensor =
ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, output_elem_size,
output_cast_ne, output_cast_nb, GGML_MAX_DIMS);
aclTensor* acl_dst_tensor = ggml_cann_create_tensor(dst);
aclnn_cast(ctx, acl_output_tensor, acl_dst_tensor, ACL_FLOAT);
ACL_CHECK(aclDestroyTensor(acl_output_tensor));
ACL_CHECK(aclDestroyTensor(acl_dst_tensor));
}
/**
* @brief Performs matrix multiplication with quantized weights and
* floating-point inputs using the CANN backend.
*
* This function performs matrix multiplication of the input tensor `src1` and
* the weight tensor `src0`, handling broadcasting, transposing, and
* quantization as needed, and stores the result in the destination tensor
* `dst`.
*
* @param ctx The context for the CANN backend operations.
* @param dst The destination tensor where the result of the matrix
* multiplication will be stored.
*/
static void ggml_cann_mul_mat_quant2(ggml_backend_cann_context& ctx,
ggml_tensor* dst,
const enum ggml_type type) {
ggml_tensor* src0 = dst->src[0]; // weight
@ -2979,11 +2786,11 @@ void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
switch (type) {
case GGML_TYPE_F32:
case GGML_TYPE_F16:
ggml_cann_mat_mul_fp2(ctx, dst);
ggml_cann_mat_mul_fp(ctx, dst);
break;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q8_0:
ggml_cann_mul_mat_quant2(ctx, dst, type);
ggml_cann_mul_mat_quant(ctx, dst, type);
break;
default:
GGML_ABORT("fatal error");

32
ggml/src/ggml-cann/ggml-cann.cpp
View file

@ -341,7 +341,6 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool {
std::vector<void*> map_offsets;
/**
* @brief Constructor to initialize the buffer pool with virtual memory for
* @brief Constructor to initialize the buffer pool with virtual memory for
* a specific device.
*
@ -1872,17 +1871,17 @@ struct ggml_backend_cann_device_context {
};
static const char * ggml_backend_cann_device_get_name(ggml_backend_dev_t dev) {
ggml_backend_cann_context * ctx = (ggml_backend_cann_context *)dev->context;
ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context;
return ctx->name.c_str();
}
static const char* ggml_backend_cann_device_get_description(ggml_backend_dev_t dev) {
ggml_backend_cann_context * ctx = (ggml_backend_cann_context *)dev->context;
ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context;
return ctx->description.c_str();
}
static void ggml_backend_cann_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
ggml_backend_cann_context * ctx = (ggml_backend_cann_context *)dev->context;
ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context;
ggml_backend_cann_get_device_memory(ctx->device, free, total);
}
@ -1909,7 +1908,7 @@ static void ggml_backend_cann_device_get_props(ggml_backend_dev_t dev, ggml_back
static ggml_backend_t ggml_backend_cann_device_init(ggml_backend_dev_t dev, const char * params) {
GGML_UNUSED(params);
ggml_backend_cann_context * ctx = (ggml_backend_cann_context *)dev->context;
ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context;
return ggml_backend_cann_init(ctx->device);
}
@ -1929,7 +1928,7 @@ static ggml_backend_t ggml_backend_cann_device_init(ggml_backend_dev_t dev, cons
static bool ggml_backend_cann_supports_buft(
ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
if (ggml_backend_buft_is_cann(buft)) {
ggml_backend_cann_context * dev_ctx = (ggml_backend_cann_context *)dev->context;
ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *)dev->context;
ggml_backend_cann_buffer_type_context * buft_ctx =
(ggml_backend_cann_buffer_type_context *)buft->context;
return buft_ctx->device == dev_ctx->device;
@ -1938,7 +1937,7 @@ static bool ggml_backend_cann_supports_buft(
}
static ggml_backend_buffer_type_t ggml_backend_cann_device_get_buffer_type(ggml_backend_dev_t dev) {
ggml_backend_cann_context * ctx = (ggml_backend_cann_context*)dev->context;
ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context;
return ggml_backend_cann_buffer_type(ctx->device);
}
@ -1959,7 +1958,7 @@ static ggml_backend_buffer_type_t ggml_backend_cann_device_get_host_buffer_type(
*/
static ggml_backend_event_t ggml_backend_cann_device_event_new(
ggml_backend_dev_t dev) {
ggml_backend_cann_context * dev_ctx = (ggml_backend_cann_context *)dev->context;
ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *)dev->context;
ggml_cann_set_device(dev_ctx->device);
@ -2067,7 +2066,11 @@ ggml_backend_reg_t ggml_backend_cann_reg() {
ggml_backend_cann_reg_context * ctx = new ggml_backend_cann_reg_context;
for (int i = 0; i < ggml_cann_info().device_count; i++) {
ggml_backend_cann_context* dev_ctx = new ggml_backend_cann_context(i);
ggml_backend_cann_device_context* dev_ctx = new ggml_backend_cann_device_context();
dev_ctx->description = aclrtGetSocName();
dev_ctx->device = i;
dev_ctx->name = GGML_CANN_NAME + std::to_string(i);
ggml_cann_set_device(i);
ggml_backend_dev_t dev = new ggml_backend_device {
/* .interface = */ ggml_backend_cann_device_interface,
/* .reg = */ &reg,
@ -2095,12 +2098,17 @@ ggml_backend_t ggml_backend_cann_init(int32_t device) {
return nullptr;
}
ggml_backend_dev_t dev = ggml_backend_reg_dev_get(ggml_backend_cann_reg(), device);
ggml_backend_cann_context* ctx = new ggml_backend_cann_context(device);
if (ctx == nullptr) {
GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__);
return nullptr;
}
ggml_cann_set_device(ctx->device);
ggml_backend_t cann_backend =
new ggml_backend{/* .guid = */ ggml_backend_cann_guid(),
/* .interface = */ ggml_backend_cann_interface,
/* .device = */ dev,
/* .context = */ dev->context};
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), device),
/* .context = */ ctx};
return cann_backend;
}