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Merge branch 'master' into concedo_experimental

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# Conflicts:
#	.devops/nix/package.nix
#	README.md
#	ggml-metal.m
#	llama.cpp
#	scripts/sync-ggml.last
#	tests/test-backend-ops.cpp
This commit is contained in:
Concedo
2024-03-05 15:54:05 +08:00
parent c952b4f192 6a87ac3a52
commit ac43e0115c
28 changed files with 1225 additions and 309 deletions
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ggml.c
+223 -14
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@@ -320,6 +320,17 @@ static ggml_fp16_t ggml_table_exp_f16[1 << 16];
// precomputed f32 table for f16 (256 KB) (ggml-impl.h)
float ggml_table_f32_f16[1 << 16];
const char * ggml_status_to_string(enum ggml_status status) {
switch (status) {
case GGML_STATUS_ALLOC_FAILED: return "GGML status: error (failed to allocate memory)";
case GGML_STATUS_FAILED: return "GGML status: error (operation failed)";
case GGML_STATUS_SUCCESS: return "GGML status: success";
case GGML_STATUS_ABORTED: return "GGML status: warning (operation aborted)";
}
return "GGML status: unknown";
}
// note: do not use these inside ggml.c
// these are meant to be used via the ggml.h API
float ggml_fp16_to_fp32(ggml_fp16_t x) {
@@ -1822,6 +1833,8 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"POOL_2D",
"UPSCALE",
"PAD",
"ARANGE",
"TIMESTEP_EMBEDDING",
"ARGSORT",
"LEAKY_RELU",
@@ -1850,7 +1863,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"CROSS_ENTROPY_LOSS_BACK",
};
static_assert(GGML_OP_COUNT == 72, "GGML_OP_COUNT != 72");
static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@@ -1908,6 +1921,8 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"pool_2d(x)",
"upscale(x)",
"pad(x)",
"arange(start, stop, step)",
"timestep_embedding(timesteps, dim, max_period)",
"argsort(x)",
"leaky_relu(x)",
@@ -1936,7 +1951,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"cross_entropy_loss_back(x,y)",
};
static_assert(GGML_OP_COUNT == 72, "GGML_OP_COUNT != 72");
static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
@@ -2896,11 +2911,21 @@ static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_
return ((const int32_t *)(tensor->op_params))[i];
}
static float ggml_get_op_params_f32(const struct ggml_tensor * tensor, uint32_t i) {
assert(i < GGML_MAX_OP_PARAMS / sizeof(float));
return ((const float *)(tensor->op_params))[i];
}
static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) {
assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t));
((int32_t *)(tensor->op_params))[i] = value;
}
static void ggml_set_op_params_f32(struct ggml_tensor * tensor, uint32_t i, float value) {
assert(i < GGML_MAX_OP_PARAMS / sizeof(float));
((float *)(tensor->op_params))[i] = value;
}
struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) {
memset(tensor->data, 0, ggml_nbytes(tensor));
return tensor;
@@ -5899,6 +5924,55 @@ struct ggml_tensor * ggml_upscale(
return ggml_upscale_impl(ctx, a, scale_factor);
}
struct ggml_tensor * ggml_arange(
struct ggml_context * ctx,
float start,
float stop,
float step) {
GGML_ASSERT(stop > start);
const int64_t steps = (int64_t) ceilf((stop - start) / step);
struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, steps);
result->op = GGML_OP_ARANGE;
ggml_set_op_params_f32(result, 0, start);
ggml_set_op_params_f32(result, 1, stop);
ggml_set_op_params_f32(result, 2, step);
return result;
}
struct ggml_tensor * ggml_timestep_embedding(
struct ggml_context * ctx,
struct ggml_tensor * timesteps,
int dim,
int max_period) {
bool is_node = false;
if (timesteps->grad) {
GGML_ASSERT(false); // TODO: implement backward
is_node = true;
}
int actual_dim = dim;
if (dim % 2 != 0) {
actual_dim = dim + 1;
}
struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, actual_dim, timesteps->ne[0]);
result->op = GGML_OP_TIMESTEP_EMBEDDING;
ggml_set_op_params_i32(result, 0, dim);
ggml_set_op_params_i32(result, 1, max_period);
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = timesteps;
return result;
}
// ggml_argsort
struct ggml_tensor * ggml_argsort(
@@ -10232,7 +10306,7 @@ static void ggml_compute_forward_group_norm_f32(
int n_channels = src0->ne[2];
int n_groups = dst->op_params[0];
int n_channels_per_group = (n_channels + n_groups - 1) / n_groups;
for (int i = ith; i < n_groups; i+=nth) {
for (int i = ith; i < n_groups; i += nth) {
int start = i * n_channels_per_group;
int end = start + n_channels_per_group;
if (end > n_channels) {
@@ -10246,28 +10320,32 @@ static void ggml_compute_forward_group_norm_f32(
for (int64_t i01 = 0; i01 < ne01; i01++) {
const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03);
ggml_float sumr = 0.0;
for (int64_t i00 = 0; i00 < ne00; i00++) {
sum += (ggml_float)x[i00];
sumr += (ggml_float)x[i00];
}
sum += sumr;
}
}
float mean = sum / (ne00 * ne01 * step);
ggml_float sum2 = 0.0;
const float mean = sum / (ne00 * ne01 * step);
ggml_float sum2 = 0.0;
for (int64_t i02 = start; i02 < end; i02++) {
for (int64_t i01 = 0; i01 < ne01; i01++) {
const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03);
float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3);
ggml_float sumr = 0.0;
for (int64_t i00 = 0; i00 < ne00; i00++) {
float v = x[i00] - mean;
y[i00] = v;
sum2 += (ggml_float)(v * v);
sumr += (ggml_float)(v * v);
}
sum2 += sumr;
}
}
float variance = sum2 / (ne00 * ne01 * step);
const float variance = sum2 / (ne00 * ne01 * step);
const float scale = 1.0f / sqrtf(variance + eps);
for (int64_t i02 = start; i02 < end; i02++) {
@@ -13548,6 +13626,106 @@ static void ggml_compute_forward_pad(
}
}
// ggml_compute_forward_arange
static void ggml_compute_forward_arange_f32(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
return;
}
GGML_ASSERT(dst->nb[0] == sizeof(float));
const int ith = params->ith;
const int nth = params->nth;
const float start = ggml_get_op_params_f32(dst, 0);
const float stop = ggml_get_op_params_f32(dst, 1);
const float step = ggml_get_op_params_f32(dst, 2);
const int64_t steps = (int64_t) ceilf((stop - start) / step);
GGML_ASSERT(ggml_nelements(dst) == steps);
for (int64_t i = ith; i < steps; i+= nth) {
float value = start + step * i;
((float *)dst->data)[i] = value;
}
}
static void ggml_compute_forward_arange(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
switch (dst->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_arange_f32(params, dst);
} break;
default:
{
GGML_ASSERT(false);
} break;
}
}
static void ggml_compute_forward_timestep_embedding_f32(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
return;
}
const struct ggml_tensor * src0 = dst->src[0];
GGML_ASSERT(src0->nb[0] == sizeof(float));
const int ith = params->ith;
const int nth = params->nth;
GGML_TENSOR_UNARY_OP_LOCALS
const int dim = ggml_get_op_params_i32(dst, 0);
const int max_period = ggml_get_op_params_i32(dst, 1);
int half = dim / 2;
for (int64_t i = 0; i < ne00; i++) {
float * embed_data = (float *)((char *) dst->data + i*nb1);
for (int64_t j = ith; j < half; j += nth) {
float timestep = ((float *)src0->data)[i];
float freq = (float)expf(-logf(max_period) * j / half);
float arg = timestep * freq;
embed_data[j] = cosf(arg);
embed_data[j + half] = sinf(arg);
}
if (dim % 2 != 0 && ith == 0) {
embed_data[dim] = 0.f;
}
}
}
static void ggml_compute_forward_timestep_embedding(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
switch (src0->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_timestep_embedding_f32(params, dst);
} break;
default:
{
GGML_ASSERT(false);
} break;
}
}
// ggml_compute_forward_argsort
static void ggml_compute_forward_argsort_f32(
@@ -15616,6 +15794,14 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_pad(params, tensor);
} break;
case GGML_OP_ARANGE:
{
ggml_compute_forward_arange(params, tensor);
} break;
case GGML_OP_TIMESTEP_EMBEDDING:
{
ggml_compute_forward_timestep_embedding(params, tensor);
} break;
case GGML_OP_ARGSORT:
{
ggml_compute_forward_argsort(params, tensor);
@@ -16618,6 +16804,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_ARANGE:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_TIMESTEP_EMBEDDING:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_ARGSORT:
{
GGML_ASSERT(false); // TODO: not implemented
@@ -17218,6 +17412,7 @@ struct ggml_compute_state {
ggml_thread_t thrd;
int ith;
struct ggml_compute_state_shared * shared;
enum ggml_status ec;
};
static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const struct ggml_compute_state_shared * st) {
@@ -17369,6 +17564,14 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
{
n_tasks = n_threads;
} break;
case GGML_OP_ARANGE:
{
n_tasks = n_threads;
} break;
case GGML_OP_TIMESTEP_EMBEDDING:
{
n_tasks = n_threads;
} break;
case GGML_OP_ARGSORT:
{
n_tasks = n_threads;
@@ -17503,7 +17706,8 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
while (true) {
if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) {
state->shared->node_n += 1;
return (thread_ret_t) GGML_EXIT_ABORTED;
state->ec = GGML_STATUS_ABORTED;
return 0;
}
if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
@@ -17625,7 +17829,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
}
}
return GGML_EXIT_SUCCESS;
return 0;
}
struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threads) {
@@ -17821,7 +18025,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
return cplan;
}
int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
{
GGML_ASSERT(cplan);
GGML_ASSERT(cplan->n_threads > 0);
@@ -17865,6 +18069,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
.thrd = 0,
.ith = j,
.shared = &state_shared,
.ec = GGML_STATUS_SUCCESS,
};
const int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]);
@@ -17875,12 +18080,14 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
workers[0].ith = 0;
workers[0].shared = &state_shared;
workers[0].ec = GGML_STATUS_SUCCESS;
const int64_t perf_start_cycles = ggml_perf_cycles();
const int64_t perf_start_time_us = ggml_perf_time_us();
// this is a work thread too
int compute_status = (size_t) ggml_graph_compute_thread(&workers[0]);
ggml_graph_compute_thread(&workers[0]);
enum ggml_status compute_status = workers[0].ec;
// don't leave affinity set on the main thread
clear_numa_thread_affinity();
@@ -17890,6 +18097,8 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
for (int j = 1; j < n_threads; j++) {
const int rc = ggml_thread_join(workers[j].thrd, NULL);
GGML_ASSERT(rc == 0);
if (workers[j].ec != GGML_STATUS_SUCCESS)
compute_status = workers[j].ec;
}
}
@@ -17917,14 +18126,14 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
return compute_status;
}
void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) {
enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) {
struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads);
struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size);
cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
ggml_graph_compute(cgraph, &cplan);
return ggml_graph_compute(cgraph, &cplan);
}
struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name) {