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ggml : fix quants nans when all the group weights are very close to zero (#7313)

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slaren
2024-05-18 02:39:54 +02:00
committed by GitHub
parent ef277de2ad
commit 05834841dc
2 changed files with 36 additions and 12 deletions
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18
tests/test-backend-ops.cpp
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@@ -16,6 +16,7 @@
#include <thread>
#include <vector>
static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) {
// static RNG initialization (revisit if n_threads stops being constant)
static const size_t n_threads = std::thread::hardware_concurrency();
@@ -49,6 +50,22 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
t.join();
}
#if 0
const char * val_str = getenv("GGML_TEST_EPS");
float val = 1e-9f;
if (val_str != nullptr) {
val = std::stof(val_str);
printf("GGML_TEST_EPS=%e\n", val);
}
// test quantization with very small values that may result in nan scales due to division by zero
if (ggml_is_quantized(tensor->type)) {
for (int i = 0; i < 256; i++) {
data[i] = val;
}
}
#endif
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 || tensor->type == GGML_TYPE_BF16) {
@@ -64,6 +81,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
}
}
ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], im);
GGML_ASSERT(ggml_validate_row_data(tensor->type, dataq.data(), dataq.size()));
ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size());
} else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) {
// This is going to create some weird integers though.