micheleandClaude Sonnet 5
9b5d1055b8
Adds tools/neural_sim/, a NumPy-based reference implementation of the FPGA-Neural V2 numeric model (INT8 in/weight, 32-bit wraparound accumulation, ReLU+saturate out), derived directly from hardware/v2/rtl/neural_processor.v (not assumed) and reusing tools/validation/mac_oracle.py's own pre-existing, hand-verified two's-complement primitives rather than duplicating them. Provides: neuron/layer/network models, a logical memory model of the real V2 SDRAM map (weights/activations/results), deterministic test-vector generators (simple/signed/extremes/zero/random/D-Stress 256x128) with JSON golden-vector export, an FPGA-vs-Python bit-exact comparison utility, four example networks, a CLI (`python -m tools.neural_sim ...`), and a 96-test pytest suite (all passing) covering signed-arithmetic edge cases (including a direct 32-bit wraparound proof), scalar-vs-vectorized neuron cross-checks, layer/memory/vector/comparison tests. This is a golden functional reference (bit-exact numeric result), explicitly NOT a cycle-accurate FPGA simulator -- see tools/neural_sim/README.md for the full scope statement. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_013xXuuRUWZScuo1DeYJxs3v
37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
import numpy as np
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import pytest
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from tools.neural_sim import examples as ex
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from tools.neural_sim.layer import FCLayer
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from tools.neural_sim.network import Network
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def test_network_rejects_empty():
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with pytest.raises(ValueError):
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Network([])
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def test_network_rejects_shape_mismatch():
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layer1 = FCLayer([[1] * 8]) # 8 -> 1
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layer2 = FCLayer([[1] * 8, [2] * 8]) # 8 -> 2, but layer1 outputs only 1
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with pytest.raises(ValueError):
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Network([layer1, layer2])
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@pytest.mark.parametrize("name", list(ex.ALL_EXAMPLES.keys()))
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def test_all_examples_run_and_stay_in_int8_range(name):
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net = ex.ALL_EXAMPLES[name]()
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rng = np.random.default_rng(123)
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inputs = rng.integers(-128, 128, size=net.n_inputs, dtype=np.int64)
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outputs = net.forward(inputs)
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assert len(outputs) == net.n_outputs
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assert all(-128 <= v <= 127 for v in outputs.tolist())
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def test_network_forward_all_matches_forward_final():
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net = ex.example_8_8_1()
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inputs = np.array([1, 2, 3, 4, -1, -2, -3, -4], dtype=np.int64)
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all_outputs = net.forward_all(inputs)
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assert all_outputs[-1].tolist() == net.forward(inputs).tolist()
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assert len(all_outputs) == len(net.layers)
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