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-rw-r--r--ggml.c775
1 files changed, 356 insertions, 419 deletions
diff --git a/ggml.c b/ggml.c
index 9ee4a8d..960b805 100644
--- a/ggml.c
+++ b/ggml.c
@@ -3440,7 +3440,9 @@ inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float
//inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] *= v; }
inline static void ggml_vec_scale_f32(const int n, float * y, const float v) {
-#if defined(GGML_SIMD)
+#if defined(GGML_USE_ACCELERATE)
+ vDSP_vsmul(y, 1, &v, y, 1, n);
+#elif defined(GGML_SIMD)
const int np = (n & ~(GGML_F32_STEP - 1));
GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);
@@ -3603,7 +3605,7 @@ inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) {
#endif
}
-inline static void ggml_vec_sum_ggf(const int n, ggml_float * s, const float * x) {
+inline static void ggml_vec_sum_f32_ggf(const int n, ggml_float * s, const float * x) {
ggml_float sum = 0.0;
for (int i = 0; i < n; ++i) {
sum += (ggml_float)x[i];
@@ -3611,6 +3613,14 @@ inline static void ggml_vec_sum_ggf(const int n, ggml_float * s, const float * x
*s = sum;
}
+inline static void ggml_vec_sum_f16_ggf(const int n, float * s, const ggml_fp16_t * x) {
+ float sum = 0.0f;
+ for (int i = 0; i < n; ++i) {
+ sum += GGML_FP16_TO_FP32(x[i]);
+ }
+ *s = sum;
+}
+
inline static void ggml_vec_max_f32(const int n, float * s, const float * x) {
#ifndef GGML_USE_ACCELERATE
float max = -INFINITY;
@@ -3750,16 +3760,6 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"ARGMAX",
"REPEAT",
"REPEAT_BACK",
- "ABS",
- "SGN",
- "NEG",
- "STEP",
- "TANH",
- "ELU",
- "RELU",
- "GELU",
- "GELU_QUICK",
- "SILU",
"SILU_BACK",
"NORM",
"RMS_NORM",
@@ -3798,6 +3798,8 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"WIN_PART",
"WIN_UNPART",
+ "UNARY",
+
"MAP_UNARY",
"MAP_BINARY",
@@ -3809,7 +3811,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"CROSS_ENTROPY_LOSS_BACK",
};
-static_assert(GGML_OP_COUNT == 68, "GGML_OP_COUNT != 68");
+static_assert(GGML_OP_COUNT == 59, "GGML_OP_COUNT != 59");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@@ -3830,16 +3832,6 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"argmax(x)",
"repeat(x)",
"repeat_back(x)",
- "abs(x)",
- "sgn(x)",
- "-x",
- "step(x)",
- "tanh(x)",
- "elu(x)",
- "relu(x)",
- "gelu(x)",
- "gelu_quick(x)",
- "silu(x)",
"silu_back(x)",
"norm(x)",
"rms_norm(x)",
@@ -3878,6 +3870,8 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"win_part(x)",
"win_unpart(x)",
+ "unary(x)",
+
"f(x)",
"f(x,y)",
@@ -3889,7 +3883,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"cross_entropy_loss_back(x,y)",
};
-static_assert(GGML_OP_COUNT == 68, "GGML_OP_COUNT != 68");
+static_assert(GGML_OP_COUNT == 59, "GGML_OP_COUNT != 59");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
@@ -4145,6 +4139,10 @@ const char * ggml_op_name(enum ggml_op op) {
return GGML_OP_NAME[op];
}
+const char * ggml_op_symbol(enum ggml_op op) {
+ return GGML_OP_SYMBOL[op];
+}
+
size_t ggml_element_size(const struct ggml_tensor * tensor) {
return GGML_TYPE_SIZE[tensor->type];
}
@@ -4443,6 +4441,10 @@ size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch)
return result;
}
+bool ggml_get_no_alloc(struct ggml_context * ctx) {
+ return ctx->no_alloc;
+}
+
void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc) {
ctx->no_alloc = no_alloc;
}
@@ -4480,7 +4482,7 @@ size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) {
// this is an error prone process, but it is necessary to support inplace
// operators when using scratch buffers
// TODO: implement a better way
-void ggml_scratch_save(struct ggml_context * ctx) {
+static void ggml_scratch_save(struct ggml_context * ctx) {
// this is needed to allow opt tensors to store their data
// TODO: again, need to find a better way
ctx->no_alloc_save = ctx->no_alloc;
@@ -4490,7 +4492,7 @@ void ggml_scratch_save(struct ggml_context * ctx) {
ctx->scratch.data = NULL;
}
-void ggml_scratch_load(struct ggml_context * ctx) {
+static void ggml_scratch_load(struct ggml_context * ctx) {
ctx->no_alloc = ctx->no_alloc_save;
ctx->scratch = ctx->scratch_save;
@@ -4498,7 +4500,7 @@ void ggml_scratch_load(struct ggml_context * ctx) {
////////////////////////////////////////////////////////////////////////////////
-struct ggml_tensor * ggml_new_tensor_impl(
+static struct ggml_tensor * ggml_new_tensor_impl(
struct ggml_context * ctx,
enum ggml_type type,
int n_dims,
@@ -4621,6 +4623,21 @@ struct ggml_tensor * ggml_new_tensor_impl(
return result;
}
+static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) {
+ assert(params_size <= GGML_MAX_OP_PARAMS);
+ memcpy(tensor->op_params, params, params_size);
+}
+
+static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_t i) {
+ assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t));
+ return ((const int32_t *)(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;
+}
+
struct ggml_tensor * ggml_new_tensor(
struct ggml_context * ctx,
enum ggml_type type,
@@ -4952,6 +4969,16 @@ float * ggml_get_data_f32(const struct ggml_tensor * tensor) {
return (float *)(tensor->data);
}
+enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) {
+ GGML_ASSERT(tensor->op == GGML_OP_UNARY);
+ return (enum ggml_unary_op) ggml_get_op_params_i32(tensor, 0);
+}
+
+static void ggml_set_unary_op(struct ggml_tensor * tensor, enum ggml_unary_op op) {
+ GGML_ASSERT(tensor->op = GGML_OP_UNARY);
+ ggml_set_op_params_i32(tensor, 0, (int32_t) op);
+}
+
const char * ggml_get_name(const struct ggml_tensor * tensor) {
return tensor->name;
}
@@ -4970,11 +4997,6 @@ struct ggml_tensor * ggml_format_name(struct ggml_tensor * tensor, const char *
return tensor;
}
-static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) {
- assert(params_size <= GGML_MAX_OP_PARAMS);
- memcpy(tensor->op_params, params, params_size);
-}
-
struct ggml_tensor * ggml_view_tensor(
struct ggml_context * ctx,
const struct ggml_tensor * src) {
@@ -5010,7 +5032,7 @@ struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * nam
// ggml_dup
-struct ggml_tensor * ggml_dup_impl(
+static struct ggml_tensor * ggml_dup_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
bool inplace) {
@@ -5043,7 +5065,7 @@ struct ggml_tensor * ggml_dup_inplace(
// ggml_add
-struct ggml_tensor * ggml_add_impl(
+static struct ggml_tensor * ggml_add_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
@@ -5086,7 +5108,7 @@ struct ggml_tensor * ggml_add_inplace(
// ggml_add1
-struct ggml_tensor * ggml_add1_impl(
+static struct ggml_tensor * ggml_add1_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
@@ -5126,7 +5148,7 @@ struct ggml_tensor * ggml_add1_inplace(
// ggml_acc
-struct ggml_tensor * ggml_acc_impl(
+static struct ggml_tensor * ggml_acc_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
@@ -5183,7 +5205,7 @@ struct ggml_tensor * ggml_acc_inplace(
// ggml_sub
-struct ggml_tensor * ggml_sub_impl(
+static struct ggml_tensor * ggml_sub_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
@@ -5222,7 +5244,7 @@ struct ggml_tensor * ggml_sub_inplace(
// ggml_mul
-struct ggml_tensor * ggml_mul_impl(
+static struct ggml_tensor * ggml_mul_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
@@ -5269,7 +5291,7 @@ struct ggml_tensor * ggml_mul_inplace(
// ggml_div
-struct ggml_tensor * ggml_div_impl(
+static struct ggml_tensor * ggml_div_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
@@ -5312,7 +5334,7 @@ struct ggml_tensor * ggml_div_inplace(
// ggml_sqr
-struct ggml_tensor * ggml_sqr_impl(
+static struct ggml_tensor * ggml_sqr_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
bool inplace) {
@@ -5345,7 +5367,7 @@ struct ggml_tensor * ggml_sqr_inplace(
// ggml_sqrt
-struct ggml_tensor * ggml_sqrt_impl(
+static struct ggml_tensor * ggml_sqrt_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
bool inplace) {
@@ -5379,7 +5401,7 @@ struct ggml_tensor * ggml_sqrt_inplace(
// ggml_log
-struct ggml_tensor * ggml_log_impl(
+static struct ggml_tensor * ggml_log_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
bool inplace) {
@@ -5559,333 +5581,142 @@ struct ggml_tensor * ggml_repeat_back(
// ggml_abs
-struct ggml_tensor * ggml_abs_impl(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- is_node = true;
- }
-
- struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
-
- result->op = GGML_OP_ABS;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
- result->src[0] = a;
-
- return result;
-}
-
struct ggml_tensor * ggml_abs(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_abs_impl(ctx, a, false);
+ return ggml_unary(ctx, a, GGML_UNARY_OP_ABS);
}
struct ggml_tensor * ggml_abs_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_abs_impl(ctx, a, true);
+ return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_ABS);
}
-
// ggml_sgn
-struct ggml_tensor * ggml_sgn_impl(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- is_node = true;
- }
-
- struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
-
- result->op = GGML_OP_SGN;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
- result->src[0] = a;
-
- return result;
-}
-
struct ggml_tensor * ggml_sgn(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_sgn_impl(ctx, a, false);
+ return ggml_unary(ctx, a, GGML_UNARY_OP_SGN);
}
struct ggml_tensor * ggml_sgn_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_sgn_impl(ctx, a, true);
+ return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SGN);
}
// ggml_neg
-struct ggml_tensor * ggml_neg_impl(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- is_node = true;
- }
-
- struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
-
- result->op = GGML_OP_NEG;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
- result->src[0] = a;
-
- return result;
-}
-
struct ggml_tensor * ggml_neg(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_neg_impl(ctx, a, false);
+ return ggml_unary(ctx, a, GGML_UNARY_OP_NEG);
}
struct ggml_tensor * ggml_neg_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_neg_impl(ctx, a, true);
+ return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_NEG);
}
// ggml_step
-struct ggml_tensor * ggml_step_impl(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- is_node = true;
- }
-
- struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
-
- result->op = GGML_OP_STEP;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
- result->src[0] = a;
-
- return result;
-}
-
struct ggml_tensor * ggml_step(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_step_impl(ctx, a, false);
+ return ggml_unary(ctx, a, GGML_UNARY_OP_STEP);
}
struct ggml_tensor * ggml_step_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_step_impl(ctx, a, true);
+ return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_STEP);
}
// ggml_tanh
-struct ggml_tensor * ggml_tanh_impl(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- is_node = true;
- }
-
- struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
-
- result->op = GGML_OP_TANH;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
- result->src[0] = a;
-
- return result;
-}
-
struct ggml_tensor * ggml_tanh(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_tanh_impl(ctx, a, false);
+ return ggml_unary(ctx, a, GGML_UNARY_OP_TANH);
}
struct ggml_tensor * ggml_tanh_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_tanh_impl(ctx, a, true);
+ return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_TANH);
}
// ggml_elu
-struct ggml_tensor * ggml_elu_impl(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- is_node = true;
- }
-
- struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
-
- result->op = GGML_OP_ELU;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
- result->src[0] = a;
-
- return result;
-}
-
struct ggml_tensor * ggml_elu(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_elu_impl(ctx, a, false);
+ return ggml_unary(ctx, a, GGML_UNARY_OP_ELU);
}
struct ggml_tensor * ggml_elu_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_elu_impl(ctx, a, true);
+ return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_ELU);
}
// ggml_relu
-struct ggml_tensor * ggml_relu_impl(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- is_node = true;
- }
-
- struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
-
- result->op = GGML_OP_RELU;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
- result->src[0] = a;
-
- return result;
-}
-
struct ggml_tensor * ggml_relu(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_relu_impl(ctx, a, false);
+ return ggml_unary(ctx, a, GGML_UNARY_OP_RELU);
}
struct ggml_tensor * ggml_relu_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_relu_impl(ctx, a, true);
+ return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_RELU);
}
// ggml_gelu
-struct ggml_tensor * ggml_gelu_impl(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- is_node = true;
- }
-
- struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
-
- result->op = GGML_OP_GELU;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
- result->src[0] = a;
-
- return result;
-}
-
struct ggml_tensor * ggml_gelu(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_gelu_impl(ctx, a, false);
+ return ggml_unary(ctx, a, GGML_UNARY_OP_GELU);
}
struct ggml_tensor * ggml_gelu_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_gelu_impl(ctx, a, true);
+ return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_GELU);
}
// ggml_gelu_quick
-struct ggml_tensor * ggml_gelu_quick_impl(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- is_node = true;
- }
-
- struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
-
- result->op = GGML_OP_GELU_QUICK;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
- result->src[0] = a;
-
- return result;
-}
-
struct ggml_tensor * ggml_gelu_quick(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_gelu_quick_impl(ctx, a, false);
+ return ggml_unary(ctx, a, GGML_UNARY_OP_GELU_QUICK);
}
struct ggml_tensor * ggml_gelu_quick_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_gelu_quick_impl(ctx, a, true);
+ return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_GELU_QUICK);
}
// ggml_silu
-struct ggml_tensor * ggml_silu_impl(
- struct ggml_context * ctx,
- struct ggml_tensor * a,
- bool inplace) {
- bool is_node = false;
-
- if (!inplace && (a->grad)) {
- is_node = true;
- }
-
- struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
-
- result->op = GGML_OP_SILU;
- result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
- result->src[0] = a;
-
- return result;
-}
-
struct ggml_tensor * ggml_silu(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_silu_impl(ctx, a, false);
+ return ggml_unary(ctx, a, GGML_UNARY_OP_SILU);
}
struct ggml_tensor * ggml_silu_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
- return ggml_silu_impl(ctx, a, true);
+ return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SILU);
}
// ggml_silu_back
@@ -5913,7 +5744,7 @@ struct ggml_tensor * ggml_silu_back(
// ggml_norm
-struct ggml_tensor * ggml_norm_impl(
+static struct ggml_tensor * ggml_norm_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
bool inplace) {
@@ -5947,7 +5778,7 @@ struct ggml_tensor * ggml_norm_inplace(
return ggml_norm_impl(ctx, a, true);
}
-struct ggml_tensor * ggml_rms_norm_impl(
+static struct ggml_tensor * ggml_rms_norm_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
bool inplace) {
@@ -6056,7 +5887,7 @@ struct ggml_tensor * ggml_out_prod(
// ggml_scale
-struct ggml_tensor * ggml_scale_impl(
+static struct ggml_tensor * ggml_scale_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
@@ -6096,7 +5927,7 @@ struct ggml_tensor * ggml_scale_inplace(
// ggml_set
-struct ggml_tensor * ggml_set_impl(
+static struct ggml_tensor * ggml_set_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
@@ -6186,7 +6017,7 @@ struct ggml_tensor * ggml_set_2d_inplace(
// ggml_cpy
-struct ggml_tensor * ggml_cpy_impl(
+static struct ggml_tensor * ggml_cpy_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
@@ -6231,7 +6062,7 @@ struct ggml_tensor * ggml_cpy_inplace(
// ggml_cont
-struct ggml_tensor * ggml_cont_impl(
+static struct ggml_tensor * ggml_cont_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
bool inplace) {
@@ -6701,7 +6532,7 @@ struct ggml_tensor * ggml_diag(
// ggml_diag_mask_inf
-struct ggml_tensor * ggml_diag_mask_inf_impl(
+static struct ggml_tensor * ggml_diag_mask_inf_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
@@ -6741,7 +6572,7 @@ struct ggml_tensor * ggml_diag_mask_inf_inplace(
// ggml_diag_mask_zero
-struct ggml_tensor * ggml_diag_mask_zero_impl(
+static struct ggml_tensor * ggml_diag_mask_zero_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
@@ -6780,7 +6611,7 @@ struct ggml_tensor * ggml_diag_mask_zero_inplace(
// ggml_soft_max
-struct ggml_tensor * ggml_soft_max_impl(
+static struct ggml_tensor * ggml_soft_max_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
bool inplace) {
@@ -6814,7 +6645,7 @@ struct ggml_tensor * ggml_soft_max_inplace(
// ggml_soft_max_back
-struct ggml_tensor * ggml_soft_max_back_impl(
+static struct ggml_tensor * ggml_soft_max_back_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
@@ -6851,7 +6682,7 @@ struct ggml_tensor * ggml_soft_max_back_inplace(
// ggml_rope
-struct ggml_tensor * ggml_rope_impl(
+static struct ggml_tensor * ggml_rope_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
@@ -7363,9 +7194,47 @@ struct ggml_tensor * ggml_win_unpart(
return result;
}
+// gmml_unary
+
+static struct ggml_tensor * ggml_unary_impl(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ enum ggml_unary_op op,
+ bool inplace) {
+ bool is_node = false;
+
+ if (!inplace && (a->grad)) {
+ is_node = true;
+ }
+
+ struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+ ggml_set_unary_op(result, op);
+
+ result->op = GGML_OP_UNARY;
+ result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+ result->src[0] = a;
+
+ return result;
+}
+
+struct ggml_tensor * ggml_unary(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ enum ggml_unary_op op) {
+ return ggml_unary_impl(ctx, a, op, false);
+}
+
+struct ggml_tensor * ggml_unary_inplace(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ enum ggml_unary_op op) {
+ return ggml_unary_impl(ctx, a, op, true);
+}
+
// ggml_map_unary
-struct ggml_tensor * ggml_map_unary_impl_f32(
+static struct ggml_tensor * ggml_map_unary_impl_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_unary_op_f32_t fun,
@@ -7403,7 +7272,7 @@ struct ggml_tensor * ggml_map_unary_inplace_f32(
// ggml_map_binary
-struct ggml_tensor * ggml_map_binary_impl_f32(
+static struct ggml_tensor * ggml_map_binary_impl_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
@@ -7447,7 +7316,7 @@ struct ggml_tensor * ggml_map_binary_inplace_f32(
// ggml_map_custom1
-struct ggml_tensor * ggml_map_custom1_impl_f32(
+static struct ggml_tensor * ggml_map_custom1_impl_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_custom1_op_f32_t fun,
@@ -7485,7 +7354,7 @@ struct ggml_tensor * ggml_map_custom1_inplace_f32(
// ggml_map_custom2
-struct ggml_tensor * ggml_map_custom2_impl_f32(
+static struct ggml_tensor * ggml_map_custom2_impl_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
@@ -7527,7 +7396,7 @@ struct ggml_tensor * ggml_map_custom2_inplace_f32(
// ggml_map_custom3
-struct ggml_tensor * ggml_map_custom3_impl_f32(
+static struct ggml_tensor * ggml_map_custom3_impl_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
@@ -9292,7 +9161,7 @@ static void ggml_compute_forward_sum_f32(
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
for (int64_t i01 = 0; i01 < ne01; i01++) {
- ggml_vec_sum_ggf(ne00,
+ ggml_vec_sum_f32_ggf(ne00,
&row_sum,
(float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03));
sum += row_sum;
@@ -9302,6 +9171,38 @@ static void ggml_compute_forward_sum_f32(
((float *) dst->data)[0] = sum;
}
+static void ggml_compute_forward_sum_f16(
+ const struct ggml_compute_params * params,
+ const struct ggml_tensor * src0,
+ struct ggml_tensor * dst) {
+ assert(params->ith == 0);
+ assert(ggml_is_scalar(dst));
+
+ if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+ return;
+ }
+
+ assert(src0->nb[0] == sizeof(ggml_fp16_t));
+
+ GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);
+ GGML_TENSOR_LOCALS(size_t, nb0, src0, nb);
+
+ float sum = 0;
+ float row_sum = 0;
+
+ for (int64_t i03 = 0; i03 < ne03; i03++) {
+ for (int64_t i02 = 0; i02 < ne02; i02++) {
+ for (int64_t i01 = 0; i01 < ne01; i01++) {
+ ggml_vec_sum_f16_ggf(ne00,
+ &row_sum,
+ (ggml_fp16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03));
+ sum += row_sum;
+ }
+ }
+ }
+ ((ggml_fp16_t *) dst->data)[0] = GGML_FP32_TO_FP16(sum);
+}
+
static void ggml_compute_forward_sum(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
@@ -9311,6 +9212,10 @@ static void ggml_compute_forward_sum(
{
ggml_compute_forward_sum_f32(params, src0, dst);
} break;
+ case GGML_TYPE_F16:
+ {
+ ggml_compute_forward_sum_f16(params, src0, dst);
+ } break;
default:
{
GGML_ASSERT(false);
@@ -10077,7 +9982,6 @@ static void ggml_compute_forward_silu(
}
}
-
// ggml_compute_forward_silu_back
static void ggml_compute_forward_silu_back_f32(
@@ -14122,6 +14026,62 @@ static void ggml_compute_forward_win_unpart(
}
}
+//gmml_compute_forward_unary
+
+static void ggml_compute_forward_unary(
+ const struct ggml_compute_params * params,
+ const struct ggml_tensor * src0,
+ struct ggml_tensor * dst) {
+ const enum ggml_unary_op op = ggml_get_unary_op(dst);
+
+ switch (op) {
+ case GGML_UNARY_OP_ABS:
+ {
+ ggml_compute_forward_abs(params, src0, dst);
+ } break;
+ case GGML_UNARY_OP_SGN:
+ {
+ ggml_compute_forward_sgn(params, src0, dst);
+ } break;
+ case GGML_UNARY_OP_NEG:
+ {
+ ggml_compute_forward_neg(params, src0, dst);
+ } break;
+ case GGML_UNARY_OP_STEP:
+ {
+ ggml_compute_forward_step(params, src0, dst);
+ } break;
+ case GGML_UNARY_OP_TANH:
+ {
+ ggml_compute_forward_tanh(params, src0, dst);
+ } break;
+ case GGML_UNARY_OP_ELU:
+ {
+ ggml_compute_forward_elu(params, src0, dst);
+ } break;
+ case GGML_UNARY_OP_RELU:
+ {
+ ggml_compute_forward_relu(params, src0, dst);
+ } break;
+ case GGML_UNARY_OP_GELU:
+ {
+ ggml_compute_forward_gelu(params, src0, dst);
+ } break;
+ case GGML_UNARY_OP_GELU_QUICK:
+ {
+ ggml_compute_forward_gelu_quick(params, src0, dst);
+ } break;
+ case GGML_UNARY_OP_SILU:
+ {
+ ggml_compute_forward_silu(params, src0, dst);
+ } break;
+ default:
+ {
+ GGML_ASSERT(false);
+ } break;
+ }
+}
+
// ggml_compute_forward_map_unary
static void ggml_compute_forward_map_unary_f32(
@@ -14682,46 +14642,6 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_repeat_back(params, tensor->src[0], tensor);
} break;
- case GGML_OP_ABS:
- {
- ggml_compute_forward_abs(params, tensor->src[0], tensor);
- } break;
- case GGML_OP_SGN:
- {
- ggml_compute_forward_sgn(params, tensor->src[0], tensor);
- } break;
- case GGML_OP_NEG:
- {
- ggml_compute_forward_neg(params, tensor->src[0], tensor);
- } break;
- case GGML_OP_STEP:
- {
- ggml_compute_forward_step(params, tensor->src[0], tensor);
- } break;
- case GGML_OP_TANH:
- {
- ggml_compute_forward_tanh(params, tensor->src[0], tensor);
- } break;
- case GGML_OP_ELU:
- {
- ggml_compute_forward_elu(params, tensor->src[0], tensor);
- } break;
- case GGML_OP_RELU:
- {
- ggml_compute_forward_relu(params, tensor->src[0], tensor);
- } break;
- case GGML_OP_GELU:
- {
- ggml_compute_forward_gelu(params, tensor->src[0], tensor);
- } break;
- case GGML_OP_GELU_QUICK:
- {
- ggml_compute_forward_gelu_quick(params, tensor->src[0], tensor);
- } break;
- case GGML_OP_SILU:
- {
- ggml_compute_forward_silu(params, tensor->src[0], tensor);
- } break;
case GGML_OP_SILU_BACK:
{
ggml_compute_forward_silu_back(params, tensor->src[0], tensor->src[1], tensor);
@@ -14864,6 +14784,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_win_unpart(params, tensor->src[0], tensor);
} break;
+ case GGML_OP_UNARY:
+ {
+ ggml_compute_forward_unary(params, tensor->src[0], tensor);
+ } break;
case GGML_OP_MAP_UNARY:
{
ggml_unary_op_f32_t fun;
@@ -15112,73 +15036,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
inplace);
}
} break;
- case GGML_OP_ABS:
- {
- if (src0->grad) {
- src0->grad =
- ggml_add_impl(ctx,
- src0->grad,
- ggml_mul(ctx,
- ggml_sgn(ctx, src0),
- tensor->grad),
- inplace);
- }
- } break;
- case GGML_OP_SGN:
- {
- if (src0->grad) {
- // noop
- }
- } break;
- case GGML_OP_NEG:
- {
- if (src0->grad) {
- src0->grad = ggml_sub_impl(ctx, src0->grad, tensor->grad, inplace);
- }
- } break;
- case GGML_OP_STEP:
- {
- if (src0->grad) {
- // noop
- }
- } break;
- case GGML_OP_TANH:
- {
- GGML_ASSERT(false); // TODO: not implemented
- } break;
- case GGML_OP_ELU:
- {
- GGML_ASSERT(false); // TODO: not implemented
- } break;
- case GGML_OP_RELU:
- {
- if (src0->grad) {
- src0->grad = ggml_sub_impl(ctx,
- src0->grad,
- ggml_mul(ctx,
- ggml_step(ctx, src0),
- tensor->grad),
- inplace);
- }
- } break;
- case GGML_OP_GELU:
- {
- GGML_ASSERT(false); // TODO: not implemented
- } break;
- case GGML_OP_GELU_QUICK:
- {
- GGML_ASSERT(false); // TODO: not implemented
- } break;
- case GGML_OP_SILU:
- {
- // necessary for llama
- if (src0->grad) {
- src0->grad = ggml_add_impl(ctx,
- src0->grad,
- ggml_silu_back(ctx, src0, tensor->grad),
- inplace);
- }
- } break;
case GGML_OP_SILU_BACK:
{
GGML_ASSERT(false); // TODO: not implemented
@@ -15440,9 +15297,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false),
inplace);
}
- if (src1->grad) {
- // noop
- }
} break;
case GGML_OP_DIAG_MASK_ZERO:
{
@@ -15454,9 +15308,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false),
inplace);
}
- if (src1->grad) {
- // noop
- }
} break;
case GGML_OP_SOFT_MAX:
{
@@ -15491,9 +15342,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
n_ctx),
inplace);
}
- if (src1->grad) {
- // noop
- }
} break;
case GGML_OP_ROPE_BACK:
{
@@ -15512,9 +15360,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
n_ctx),
inplace);
}
- if (src1->grad) {
- // noop
- }
} break;
case GGML_OP_ALIBI:
{
@@ -15707,6 +15552,80 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
} break;
case GGML_OP_WIN_PART:
case GGML_OP_WIN_UNPART:
+ case GGML_OP_UNARY:
+ {
+ switch (ggml_get_unary_op(tensor)) {
+ case GGML_UNARY_OP_ABS:
+ {
+ if (src0->grad) {
+ src0->grad =
+ ggml_add_impl(ctx,
+ src0->grad,
+ ggml_mul(ctx,
+ ggml_sgn(ctx, src0),
+ tensor->grad),
+ inplace);
+ }
+ } break;
+ case GGML_UNARY_OP_SGN:
+ {
+ if (src0->grad) {
+ // noop
+ }
+ } break;
+ case GGML_UNARY_OP_NEG:
+ {
+ if (src0->grad) {
+ src0->grad = ggml_sub_impl(ctx, src0->grad, tensor->grad, inplace);
+ }
+ } break;
+ case GGML_UNARY_OP_STEP:
+ {
+ if (src0->grad) {
+ // noop
+ }
+ } break;
+ case GGML_UNARY_OP_TANH:
+ {
+ GGML_ASSERT(false); // TODO: not implemented
+ } break;
+ case GGML_UNARY_OP_ELU:
+ {
+ GGML_ASSERT(false); // TODO: not implemented
+ } break;
+ case GGML_UNARY_OP_RELU:
+ {
+ if (src0->grad) {
+ src0->grad = ggml_add_impl(ctx,
+ src0->grad,
+ ggml_mul(ctx,
+ ggml_step(ctx, src0),
+ tensor->grad),
+ inplace);
+ }
+ } break;
+ case GGML_UNARY_OP_GELU:
+ {
+ GGML_ASSERT(false); // TODO: not implemented
+ } break;
+ case GGML_UNARY_OP_GELU_QUICK:
+ {
+ GGML_ASSERT(false); // TODO: not implemented
+ } break;
+ case GGML_UNARY_OP_SILU:
+ {
+ // necessary for llama
+ if (src0->grad) {
+ src0->grad = ggml_add_impl(ctx,
+ src0->grad,
+ ggml_silu_back(ctx, src0, tensor->grad),
+ inplace);
+ }
+ } break;
+ default:
+ GGML_ASSERT(false);
+ }
+ } break;
case GGML_OP_MAP_UNARY:
case GGML_OP_MAP_BINARY:
case GGML_OP_MAP_CUSTOM1:
@@ -15937,7 +15856,7 @@ typedef pthread_t ggml_thread_t;
// Android's libc implementation "bionic" does not support setting affinity
#if defined(__linux__) && !defined(__BIONIC__)
-void set_numa_thread_affinity(int thread_n, int n_threads) {
+static void set_numa_thread_affinity(int thread_n, int n_threads) {
if (!ggml_is_numa()) {
return;
}
@@ -15962,7 +15881,7 @@ void set_numa_thread_affinity(int thread_n, int n_threads) {
CPU_FREE(cpus);
}
-void clear_numa_thread_affinity(void) {
+static void clear_numa_thread_affinity(void) {
if (!ggml_is_numa()) {
return;
}
@@ -15986,8 +15905,8 @@ void clear_numa_thread_affinity(void) {
#else
// TODO: Windows etc.
// (the linux implementation may also work on BSD, someone should test)
-void set_numa_thread_affinity(int thread_n, int n_threads) { UNUSED(thread_n); UNUSED(n_threads); }
-void clear_numa_thread_affinity(void) {}
+static void set_numa_thread_affinity(int thread_n, int n_threads) { UNUSED(thread_n); UNUSED(n_threads); }
+static void clear_numa_thread_affinity(void) {}
#endif
struct ggml_compute_state_shared {
@@ -16199,21 +16118,34 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
case GGML_OP_ARGMAX:
case GGML_OP_REPEAT:
case GGML_OP_REPEAT_BACK:
- case GGML_OP_ABS:
- case GGML_OP_SGN:
- case GGML_OP_NEG:
- case GGML_OP_STEP:
- case GGML_OP_TANH:
- case GGML_OP_ELU:
- case GGML_OP_RELU:
- {
+ {
n_tasks = 1;
} break;
- case GGML_OP_MUL:
- case GGML_OP_GELU:
- case GGML_OP_GELU_QUICK:
- case GGML_OP_SILU:
+
+ case GGML_OP_UNARY:
+ {
+ switch (ggml_get_unary_op(node)) {
+ case GGML_UNARY_OP_ABS:
+ case GGML_UNARY_OP_SGN:
+ case GGML_UNARY_OP_NEG:
+ case GGML_UNARY_OP_STEP:
+ case GGML_UNARY_OP_TANH:
+ case GGML_UNARY_OP_ELU:
+ case GGML_UNARY_OP_RELU:
+ {
+ n_tasks = 1;
+ } break;
+
+ case GGML_UNARY_OP_GELU:
+ case GGML_UNARY_OP_GELU_QUICK:
+ case GGML_UNARY_OP_SILU:
+ {
+ n_tasks = n_threads;
+ } break;
+ }
+ } break;
case GGML_OP_SILU_BACK:
+ case GGML_OP_MUL:
case GGML_OP_NORM:
case GGML_OP_RMS_NORM:
case GGML_OP_RMS_NORM_BACK:
@@ -16728,7 +16660,8 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
fwrite(&nb, sizeof(uint64_t), 1, fout);
}
- fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout);
+ fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout);
+ fwrite(tensor->op_params, sizeof(char), GGML_MAX_OP_PARAMS, fout);
// dump the data
// TODO: pad this to 32 byte boundary
@@ -16761,7 +16694,8 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
fwrite(&nb, sizeof(uint64_t), 1, fout);
}
- fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout);
+ fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout);
+ fwrite(tensor->op_params, sizeof(char), GGML_MAX_OP_PARAMS, fout);
// output the op arguments
{
@@ -16942,7 +16876,8 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context **
tensor->op = (enum ggml_op) op;
- memcpy(tensor->name, ptr, GGML_MAX_NAME); ptr += GGML_MAX_NAME;
+ memcpy(tensor->name, ptr, GGML_MAX_NAME); ptr += GGML_MAX_NAME;
+ memcpy(tensor->op_params, ptr, GGML_MAX_OP_PARAMS); ptr += GGML_MAX_OP_PARAMS;
tensor->data = (void *) ptr;
@@ -16987,7 +16922,8 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context **
nb[j] = nb_cur;
}
- const char * ptr_name = ptr; ptr += GGML_MAX_NAME;
+ const char * ptr_name = ptr; ptr += GGML_MAX_NAME;
+ const char * ptr_op_params = ptr; ptr += GGML_MAX_OP_PARAMS;
const int32_t * ptr_arg_idx = (const int32_t *) ptr; ptr += GGML_MAX_SRC*sizeof(int32_t);
@@ -17024,8 +16960,8 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context **
{
tensor = ggml_view_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3], 0, 0, 0, 0);
- uint64_t offs;
- memcpy(&offs, tensor->op_params, sizeof(offs));
+ size_t offs;
+ memcpy(&offs, ptr_op_params, sizeof(offs));
tensor->data = ((char *) tensor->data) + offs;
} break;
@@ -17045,7 +16981,8 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context **
} break;
}
- memcpy(tensor->name, ptr_name, GGML_MAX_NAME);
+ memcpy(tensor->name, ptr_name, GGML_MAX_NAME);
+ memcpy(tensor->op_params, ptr_op_params, GGML_MAX_OP_PARAMS);
for (int j = 0; j < GGML_MAX_DIMS; ++j) {
tensor->nb[j] = nb[j];
@@ -17079,7 +17016,7 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) {
GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 ", %5" PRId64 "] %16s %s (%3d) cpu = %7.3f / %7.3f ms, wall = %7.3f / %7.3f ms\n",
i,
node->ne[0], node->ne[1], node->ne[2],
- GGML_OP_NAME[node->op], node->is_param ? "x" : node->grad ? "g" : " ", node->perf_runs,
+ ggml_op_name(node->op), node->is_param ? "x" : node->grad ? "g" : " ", node->perf_runs,
(double) node->perf_cycles / (double) ggml_cycles_per_ms(),
(double) node->perf_cycles / (double) ggml_cycles_per_ms() / (double) node->perf_runs,
(double) node->perf_time_us / 1000.0,
@@ -17093,7 +17030,7 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) {
GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 "] %8s\n",
i,
node->ne[0], node->ne[1],
- GGML_OP_NAME[node->op]);
+ ggml_op_name(node->op));
}
for (int i = 0; i < GGML_OP_COUNT; i++) {
@@ -17101,7 +17038,7 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) {
continue;
}
- GGML_PRINT("perf_total_per_op_us[%16s] = %7.3f ms\n", GGML_OP_NAME[i], (double) perf_total_per_op_us[i] / 1000.0);
+ GGML_PRINT("perf_total_per_op_us[%16s] = %7.3f ms\n", ggml_op_name(i), (double) perf_total_per_op_us[i] / 1000.0);
}
GGML_PRINT("========================================\n");
@@ -17195,13 +17132,13 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph
}
if (node->n_dims == 2) {
- fprintf(fp, "%d [%" PRId64 ", %" PRId64 "] | <x>%s", i, node->ne[0], node->ne[1], GGML_OP_SYMBOL[node->op]);
+ fprintf(fp, "%d [%" PRId64 ", %" PRId64 "] | <x>%s", i, node->ne[0], node->ne[1], ggml_op_symbol(node->op));
} else {
- fprintf(fp, "%d [%" PRId64 ", %" PRId64 ", %" PRId64 "] | <x>%s", i, node->ne[0], node->ne[1], node->ne[2], GGML_OP_SYMBOL[node->op]);
+ fprintf(fp, "%d [%" PRId64 ", %" PRId64 ", %" PRId64 "] | <x>%s", i, node->ne[0], node->ne[1], node->ne[2], ggml_op_symbol(node->op));
}
if (node->grad) {
- fprintf(fp, " | <g>%s\"; ]\n", GGML_OP_SYMBOL[node->grad->op]);
+ fprintf(fp, " | <g>%s\"; ]\n", ggml_op_symbol(node->grad->op));
} else {
fprintf(fp, "\"; ]\n");
}
generated by cgit v1.2.3 (git 2.25.1) at 2024-10-18 12:31:11 +0000