vbatts/llama.cpp
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remove printing logic from ggml.c

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This commit is contained in:
Phillip Kravtsov 2023-09-28 22:18:56 -07:00
parent fa92f6e827
commit c28a6c5ba0
1 changed files with 0 additions and 83 deletions
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83
ggml.c
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@ -9079,12 +9079,6 @@ static void ggml_compute_forward_dup(
struct ggml_tensor * dst) { struct ggml_tensor * dst) {
if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) { if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
ggml_compute_forward_dup_same_cont(params, src0, dst); ggml_compute_forward_dup_same_cont(params, src0, dst);
if (strncmp(src0->name, "printme_tmp_", 12) == 0 && params->ith == 0) {
GGML_PRINT("\noutputs of dupe for %s\n", src0->name);
ggml_print_tensor(dst);
int starts[] = {0, 0, 0, 0};
ggml_print_tensor_values(dst, starts, 0, 10);
}
return; return;
} }
switch (src0->type) { switch (src0->type) {
@ -9101,12 +9095,6 @@ static void ggml_compute_forward_dup(
GGML_ASSERT(false); GGML_ASSERT(false);
} break; } break;
} }
if (strncmp(src0->name, "printme_tmp_", 12) == 0 && params->ith == 0) {
GGML_PRINT("\noutputs of dupe for %s\n", src0->name);
ggml_print_tensor(dst);
int starts[] = {0, 0, 0, 0};
ggml_print_tensor_values(dst, starts, 0, 10);
}
} }
// ggml_compute_forward_add // ggml_compute_forward_add
@ -9182,14 +9170,6 @@ static void ggml_compute_forward_add_f32(
} }
} }
} }
if ((strncmp(src0->name, "printme", 7) == 0
||strncmp(src1->name, "printme", 7) == 0)
&& params->ith == 0) {
GGML_PRINT("\noutputs of add: %s + %s\n", src0->name, src1->name);
ggml_print_tensor(dst);
int starts[] = {0, 0, 0, 0};
ggml_print_tensor_values(dst, starts, 0, 10);
}
} }
static void ggml_compute_forward_add_f16_f32( static void ggml_compute_forward_add_f16_f32(
@ -11226,13 +11206,6 @@ static void ggml_compute_forward_norm_f32(
GGML_ASSERT(src0->nb[0] == sizeof(float)); GGML_ASSERT(src0->nb[0] == sizeof(float));
// If the name starts with "layer_inputs", and we are on thread 0, print the tensor // If the name starts with "layer_inputs", and we are on thread 0, print the tensor
if (strncmp(src0->name, "printme", 7) == 0
&& params->ith == 0) {
GGML_PRINT("\nlayernorm inputs for %s\n", src0->name);
ggml_print_tensor(src0);
int starts[] = {0, 1, 0};
ggml_print_tensor_values(src0, starts, 0, 10);
}
const int ith = params->ith; const int ith = params->ith;
const int nth = params->nth; const int nth = params->nth;
@ -11656,44 +11629,7 @@ static void ggml_compute_forward_mul_mat(
struct ggml_tensor * dst) { struct ggml_tensor * dst) {
int64_t t0 = ggml_perf_time_us(); int64_t t0 = ggml_perf_time_us();
UNUSED(t0); UNUSED(t0);
if ((strncmp(src0->name, "printme", 7) == 0 ||
strncmp(src1->name, "printme", 7) == 0)
&& params->ith == 0) {
GGML_PRINT("\nInputs to matmul: %s\n", src1->name);
size_t offset = 0;//(src1->ne[0] * src1->ne[1])
size_t x = src1->ne[0];
size_t y = src1->ne[1];
for (int i=0; i < x * y; ++i) {
if (i % x == 0) {
GGML_PRINT("\n");
}
if (i % x < 4) {
GGML_PRINT(" %f ", ((float *)src1->data)[i + offset]);
}
}
GGML_PRINT("\n");
/*
GGML_PRINT("\nInputs to matmul: %s\n", src0->name);
ggml_print_tensor(src0);
if (src0->type == GGML_TYPE_F16) {
for (int i=0; i < src0->ne[0] * src0->ne[1]; ++i) {
if (i % src0->ne[0] == 0) {
GGML_PRINT("\n");
}
GGML_PRINT(" %f", ((ggml_fp16_t *) src0->data)[i]);
}
}
GGML_PRINT("\n");
*/
}
<<<<<<< HEAD
GGML_TENSOR_BINARY_OP_LOCALS;
// If on thread 0, src1 starts with KQ_softmax, print
=======
GGML_TENSOR_BINARY_OP_LOCALS GGML_TENSOR_BINARY_OP_LOCALS
>>>>>>> bc39553c901a91cfcb757863586250838c83eeab
const int ith = params->ith; const int ith = params->ith;
const int nth = params->nth; const int nth = params->nth;
@ -12229,12 +12165,6 @@ static void ggml_compute_forward_scale_f32(
} }
ggml_vec_scale_f32(nc, (float *) ((char *) dst->data + i1*nb1), v); ggml_vec_scale_f32(nc, (float *) ((char *) dst->data + i1*nb1), v);
} }
if (strncmp(src0->name, "printme", 7) == 0 && params->ith == 0) {
GGML_PRINT("\nInputs of scale: %s\n", dst->name);
ggml_print_tensor(src0);
int starts[4] = {0, 0, 0, 0};
ggml_print_tensor_values(src0, starts, 0, 32);
}
} }
static void ggml_compute_forward_scale( static void ggml_compute_forward_scale(
@ -13245,12 +13175,6 @@ static void ggml_compute_forward_rope_f32(
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return; return;
} }
if (strncmp(src0->name, "printme", 7) == 0 && params->ith == 0) {
GGML_PRINT("\ninputs of RoPE for %s\n", src0->name);
ggml_print_tensor(src0);
int starts[] = {0, 0, 1, 0};
ggml_print_tensor_values(src0, starts, 0, 10);
}
float freq_base; float freq_base;
float freq_scale; float freq_scale;
@ -13379,13 +13303,6 @@ static void ggml_compute_forward_rope_f32(
} }
} }
} }
if (strncmp(src0->name, "printme", 7) == 0 && params->ith == 0) {
GGML_PRINT("\n dest at RoPE time for %s\n", src0->name);
// print shape and strides
int starts[3] = {0,0,1};
ggml_print_tensor(dst);
ggml_print_tensor_values(dst, starts, 0, 10);
}
} }
static void ggml_compute_forward_rope_f16( static void ggml_compute_forward_rope_f16(