ggml : fix quants nans when all the group weights are very close to zero (#7313)
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2 changed files with 36 additions and 12 deletions
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@ -16,6 +16,7 @@
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#include <thread>
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#include <vector>
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static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) {
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// static RNG initialization (revisit if n_threads stops being constant)
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static const size_t n_threads = std::thread::hardware_concurrency();
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@ -49,6 +50,22 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
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t.join();
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}
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#if 0
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const char * val_str = getenv("GGML_TEST_EPS");
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float val = 1e-9f;
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if (val_str != nullptr) {
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val = std::stof(val_str);
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printf("GGML_TEST_EPS=%e\n", val);
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}
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// test quantization with very small values that may result in nan scales due to division by zero
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if (ggml_is_quantized(tensor->type)) {
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for (int i = 0; i < 256; i++) {
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data[i] = val;
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}
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}
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#endif
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if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) {
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ggml_backend_tensor_set(tensor, data.data(), 0, size * sizeof(float));
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} else if (ggml_is_quantized(tensor->type) || tensor->type == GGML_TYPE_F16 || tensor->type == GGML_TYPE_BF16) {
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@ -64,6 +81,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
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}
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}
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ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], im);
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GGML_ASSERT(ggml_validate_row_data(tensor->type, dataq.data(), dataq.size()));
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ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size());
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} else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) {
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// This is going to create some weird integers though.
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