from ggml import ffi, lib
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from ggml.utils import init, numpy, copy
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import numpy as np
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from math import pi, cos, sin, ceil
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import matplotlib.pyplot as plt
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ctx = init(mem_size=100*1024*1024) # Will be auto-GC'd
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n = 256
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orig = np.array([
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[
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cos(j * 2 * pi / n) * (sin(i * 2 * pi / n))
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for j in range(n)
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]
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for i in range(n)
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], np.float32)
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orig_tensor = lib.ggml_new_tensor_2d(ctx, lib.GGML_TYPE_F32, n, n)
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copy(orig, orig_tensor)
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quants = [
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type for type in range(lib.GGML_TYPE_COUNT)
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if lib.ggml_is_quantized(type) and
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type not in [lib.GGML_TYPE_Q8_1, lib.GGML_TYPE_Q8_K] # Apparently not supported
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]
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# quants = [lib.GGML_TYPE_Q2_K] # Test a single one
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def get_name(type):
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name = lib.ggml_type_name(type)
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return ffi.string(name).decode('utf-8') if name else '?'
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quants.sort(key=get_name)
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quants.insert(0, None)
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print(quants)
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ncols=4
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nrows = ceil(len(quants) / ncols)
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plt.figure(figsize=(ncols * 5, nrows * 5), layout='tight')
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for i, type in enumerate(quants):
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plt.subplot(nrows, ncols, i + 1)
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try:
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if type == None:
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plt.title('Original')
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plt.imshow(orig)
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else:
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quantized_tensor = lib.ggml_new_tensor_2d(ctx, type, n, n)
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copy(orig_tensor, quantized_tensor)
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quantized = numpy(quantized_tensor, allow_copy=True)
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d = quantized - orig
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results = {
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"l2": np.linalg.norm(d, 2),
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"linf": np.linalg.norm(d, np.inf),
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"compression":
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round(lib.ggml_nbytes(orig_tensor) /
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lib.ggml_nbytes(quantized_tensor), 1)
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}
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name = get_name(type)
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print(f'{name}: {results}')
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plt.title(f'{name} ({results["compression"]}x smaller)')
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plt.imshow(quantized, interpolation='nearest')
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except Exception as e:
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print(f'Error: {e}')
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plt.show()
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