A standalone Vulkan inference engine (engine/) for DLSS presets K and M that runs from weights extracted from the user's own nvngx_dlss.dll, with d4r's own input, network and reconstruction shaders or the native HIP network layers, recorded into the game's command list by the shim. Opt-in ([Engine] Enabled / D4R_ENGINE=1); anything uncovered falls back to the CUDA/ZLUDA path with the reason logged. Byte-identical to NGX's CUDA backend under the phase-capable reference (1706x960 -> 2560x1440, native HIP network, eleven frames, RX 9070 XT): - K render- and display-resolution motion vectors, HDR and LDR - K game-supplied exposure and automatic exposure with any pre-exposure and exposure scale (one residual: effective exposure exactly 2 or 4) - K and M with the DLSS indicator's inverted-axis parameters - M with any exposure settings (NGX ignores them for M) K packages need the current compile_k.py (marker D4RO0003). Also: HIP shared buffers are pooled across feature recreation (ROCm leaks destroyed imports), hipnet.bin must match the preset, M-only games do not load the K model, install_d4r_runtime.sh stages k, k-ldr, m, m-ldr with hip/, the activation table has a generator (gen_activation_m.py), and package_release.sh can bundle the engine's shaders (never its weights). Signed-off-by: woutermans <[email protected]>
52 lines
2.5 KiB
Python
52 lines
2.5 KiB
Python
"""Structural model corruption must be rejected before compiling/dispatching shaders."""
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import unittest
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from compile_k import validate
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from extract_k import LAYERS
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def minimal_index():
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index = {}
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for name, (kind, h, c, x, pos) in LAYERS.items():
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e = {'kind': kind, 'H': h, 'C': c, 'X': x, 'posattn': pos,
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'g1': 0, 'bo': 0, 'g2': 0, 'b2': 0,
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'qkv': [0] * (h * 3), 'table': [0] * h, 'wo': [0] * h,
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'w1': [0] * (c // 8), 'b1': [0] * (c // 8), 'w2': [0] * (c // 8)}
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if kind in ('enc', 'enc0'): e.update(merge_n=x, merge_w=0, merge_b=0)
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if kind in ('dec', 'dec0'): e.update(expand_w=0, expand_b=0)
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if kind == 'enc0': e.update(embed_w=0, embed_b=0)
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if kind == 'dec0': e.update(head_w=0, head_b=0)
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index[name] = e
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return index
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class ModelTests(unittest.TestCase):
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def setUp(self): self.index = minimal_index()
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def test_shared_zero_weights_are_structurally_valid(self):
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validate(self.index, 4 * 1024 * 1024)
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def test_matrix_end_past_blob_is_rejected(self):
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self.index['dec4']['expand_w'] = 4 * 1024 * 1024 // 2 - 16
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with self.assertRaisesRegex(ValueError, 'outside'): validate(self.index, 4 * 1024 * 1024)
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def test_weight_alignment_is_checked(self):
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self.index['enc0']['embed_w'] = 1
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with self.assertRaisesRegex(ValueError, 'alignment'): validate(self.index, 4 * 1024 * 1024)
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def test_wrong_network_architecture_is_rejected(self):
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self.index['enc3']['H'] = 2
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with self.assertRaisesRegex(ValueError, 'shape'): validate(self.index, 4 * 1024 * 1024)
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def test_missing_merge_is_rejected(self):
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del self.index['enc2']['merge_w']
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with self.assertRaisesRegex(ValueError, 'missing'): validate(self.index, 4 * 1024 * 1024)
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def test_incomplete_head_set_is_rejected(self):
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self.index['dec5']['qkv'].pop()
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with self.assertRaisesRegex(ValueError, 'expected'): validate(self.index, 4 * 1024 * 1024)
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def test_wrong_layer_order_is_rejected(self):
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index = dict(reversed(list(self.index.items())))
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with self.assertRaisesRegex(ValueError, 'order'): validate(index, 4 * 1024 * 1024)
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def test_odd_blob_bytes_are_rejected(self):
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with self.assertRaisesRegex(ValueError, 'f16'): validate(self.index, 4 * 1024 * 1024 - 1)
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def test_invalid_merge_stride_is_rejected(self):
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self.index['enc2']['merge_n'] = 128
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with self.assertRaisesRegex(ValueError, 'stride'): validate(self.index, 4 * 1024 * 1024)
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if __name__ == '__main__': unittest.main()
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