feat: add d4r inference engines and Windows parity tooling
Add native K/M/RR inference engines, HIP kernels, replay and validation harnesses, and Windows DLSS setup and runtime support. Include GPU profiling tools, build and packaging scripts, platform integration patches, and implementation documentation.
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#!/usr/bin/env python3
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"""Generate public synthetic K replay fixtures and validate native outputs.
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Uses the upstream replay format and NumPy model for all eleven K layers.
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No NVIDIA weights, PTX or DLLs are used by this fixture generator.
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"""
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import argparse
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import math
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import pathlib
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import struct
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import sys
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import numpy as np
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ROOT = pathlib.Path(__file__).resolve().parents[2]
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REFERENCE = pathlib.Path(__file__).resolve().parent / 'reference'
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if not (REFERENCE / 'pwin_check.py').exists():
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REFERENCE = ROOT / 'kernels/tools'
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sys.path.insert(0, str(REFERENCE))
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import pwin_model as pm
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pm.NORM_FRAGMENT_ORDER = True
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from pwin_check import LAYERS, model_window, Outputs, stored, windows
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def generate(layer, directory):
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kind, heads, channels, other, extra = LAYERS[layer]
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cin = 16 if kind == 'enc0' else channels
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low = kind in ('dec', 'dec0')
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cout = other if kind in ('enc', 'enc0') else channels
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layout = pm.Layout(heads, channels, cout)
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core_base = 2 * channels + 2 * 16 * channels if kind == 'enc0' else 0
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if low:
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core_base = 2 * other * 4 * channels + 8 * channels
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weight_bytes = core_base + layout.PMB + 2 * layout.NPA * heads
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weights = np.zeros(weight_bytes, np.uint8)
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def half(offset, value):
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weights[offset:offset + 2] = np.array([value], np.float16).view(np.uint8)
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def matrix(base, k, n, stride, value=1.0):
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half(base + 512 * (k // 16) + stride * (n // 16) + pm.frag_offset(k % 16, n % 16), value)
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if kind == 'enc0':
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for k in range(16):
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matrix(2 * channels, k, k, 512)
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matrix(2 * channels, k, k + 16, 512, .5)
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if low:
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for q in range(4):
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for k in range(min(other, channels)):
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matrix(0, k, q * channels + k, 512 * (other // 16), .25)
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for c in range(channels):
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half(core_base + layout.G1 + 2 * c, 1)
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half(core_base + layout.G2 + 2 * c, 1)
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half(core_base + layout.BO + 2 * c, 1 / 64)
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half(core_base + layout.B2 + 2 * c, 1 / 64)
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position_only = kind in ('enc0', 'dec0') or bool(extra)
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for head in range(heads):
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source_group = head % (channels // 32)
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for j in range(3):
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for k in range(32):
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matrix(core_base + layout.QKV + layout.HEAD * head + 2048 * (3 * source_group + j),
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k, k, 1024, .5 if j == 2 else .25)
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for k in range(32):
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matrix(core_base + layout.WO + 64 * channels * head, k,
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32 * source_group + k, 1024, .5)
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if position_only:
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for row in range(64):
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for col in range(64):
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r, c = row % 16, col % 16
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offset = core_base + layout.BIAS + head * 8192 + 512 * (4 * (col // 16) + row // 16)
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offset += 64 * (r & 7) + 16 * ((c & 7) >> 1) + 2 * (c & 1) + 4 * (r >> 3) + 8 * (c >> 3)
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half(offset, 1 / 64)
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for group in range(channels // 32):
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for k in range(32):
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matrix(core_base + layout.W1 + 64 * channels * group, 32 * group + k, k, 32 * channels, .5)
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matrix(core_base + layout.W2 + 64 * channels * group, k, 32 * group + k, 1024, .5)
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if kind in ('enc', 'enc0'):
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for n in range(cout):
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matrix(core_base + layout.PM, n % channels + channels * ((n // channels) % 4),
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n, 128 * channels, .5)
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if kind == 'dec0':
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for n in range(40):
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matrix(core_base + layout.PM + 2 * 48, n % channels, n, 1024, .5)
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half(core_base + layout.PM + 2 * n, 1 / 64)
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input_elements = 16 * other if low else 64 * cin
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x = ((np.arange(input_elements) % 9 - 4) / 4).astype(np.float16)
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skip = ((np.arange(64 * channels) % 7 - 3) / 8).astype(np.float16)
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output_channels = 40 if kind == 'dec0' else channels
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allocations = {0: x.view(np.uint8), 1: skip.view(np.uint8), 2: weights}
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if kind in ('enc0', 'enc'):
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allocations[3] = np.full(16 * cout, np.nan, np.float16).view(np.uint8)
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allocations[4] = np.full(64 * channels, np.nan, np.float16).view(np.uint8)
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else:
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allocations[3] = np.full(64 * output_channels, np.nan, np.float16).view(np.uint8)
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parameters = bytearray(176)
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struct.pack_into('<ii', parameters, 0, 8, 8)
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pointers = {8: 0, 64: 2, 24: 3}
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if low:
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pointers[16] = 1
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if kind in ('enc0', 'enc'):
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pointers[32] = 4
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bases = {i: 0x100000000 + i * 0x1000000 for i in allocations}
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for offset, identifier in pointers.items():
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struct.pack_into('<Q', parameters, offset, bases[identifier])
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directory.mkdir(parents=True, exist_ok=True)
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for identifier, data in allocations.items():
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data.tofile(directory / f'alloc-{identifier}.bin')
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(directory / 'args.bin').write_bytes(parameters)
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lines = [f'kernel dltss_pwin_{layer}_layer', 'launch 1 1 1 32 1 4 0']
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lines += [f'alloc {i} {bases[i]:016x} {data.size}' for i, data in allocations.items()]
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lines += [f'pointer {offset} {i} 0' for offset, i in pointers.items()]
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(directory / 'manifest.txt').write_text('\n'.join(lines) + '\n', encoding='utf-8')
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print(f'PASS K_FIXTURE kernel={layer} weights={weight_bytes} public_synthetic=1')
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def validate(layer, directory, output, window_count=1, real_capture=False):
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dump = pm.Dump(str(directory))
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outputs = Outputs(dump, str(output))
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pairs = []
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for bx, by in windows(dump.grid, window_count):
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model, extra = model_window(dump, layer, bx, by)
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pairs.append(stored(dump, outputs, layer, bx, by, model, extra))
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expected, actual = (np.concatenate(values) for values in zip(*pairs))
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if not np.isfinite(expected).all() or not np.isfinite(actual).all():
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raise RuntimeError('K replay contains NaN/Inf or unwritten output')
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difference = np.abs(actual.astype(np.float64) - expected.astype(np.float64))
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maximum = float(difference.max())
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relative = float((difference / np.maximum(np.abs(expected.astype(np.float64)), 1e-3)).max())
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rms = float(np.sqrt(np.mean(difference ** 2)))
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peak = max(1., float(np.abs(expected.astype(np.float64)).max()))
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psnr = math.inf if not rms else 20 * math.log10(peak / rms)
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print(f'K_REPLAY_REFERENCE kernel={layer} elements={actual.size} max_abs={maximum:.9g} '
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f'max_rel={relative:.9g} rms={rms:.9g} psnr_db={psnr:.6g}')
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# Bound ISA approximate reciprocal/rsqrt and matrix accumulation differences;
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# require the independent PTX reference gate for every real-weight layer.
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absolute_limit = max(.001, .005 * peak) if real_capture else .001
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print(f'K_REPLAY_LIMIT windows={len(pairs)} max_abs_limit={absolute_limit:.9g} min_psnr_db=60 real_capture={int(real_capture)}')
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if maximum > absolute_limit or psnr < 60:
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worst = int(difference.argmax())
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raise RuntimeError(f'K mismatch at {worst}: reference={expected[worst]}, gpu={actual[worst]}')
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print(f'PASS K_REPLAY_REFERENCE kernel={layer} reference=logical nan_inf=0')
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('mode', choices=['generate', 'validate'])
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parser.add_argument('--layer', choices=list(LAYERS), required=True)
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parser.add_argument('--fixture-dir', type=pathlib.Path, required=True)
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parser.add_argument('--output-dir', type=pathlib.Path)
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parser.add_argument('--windows', type=int, default=1)
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parser.add_argument('--real-capture', action='store_true')
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args = parser.parse_args()
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try:
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if args.mode == 'generate':
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generate(args.layer, args.fixture_dir)
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else:
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if not args.output_dir:
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parser.error('validate requires --output-dir')
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if args.windows < 1:
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parser.error('--windows must be positive')
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validate(args.layer, args.fixture_dir, args.output_dir, args.windows, args.real_capture)
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except Exception as error:
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print(f'FAIL K_REPLAY {error}', file=sys.stderr)
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raise SystemExit(5)
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