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Submodule example-models
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,171 @@ | ||
| from pathlib import Path | ||
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| import HGQ # noqa: F401 | ||
| import numpy as np | ||
| import pytest | ||
| import tensorflow as tf | ||
| from HGQ import get_default_paq_conf, set_default_paq_conf, trace_minmax | ||
| from HGQ.layers import ( # noqa: F401 | ||
| HConv1D, | ||
| HDense, | ||
| HQuantize, | ||
| PAvgPool1D, | ||
| PAvgPool2D, | ||
| PConcatenate, | ||
| PFlatten, | ||
| PMaxPool1D, | ||
| PMaxPool2D, | ||
| PReshape, | ||
| Signature, | ||
| ) | ||
| from HGQ.proxy import to_proxy_model | ||
| from HGQ.proxy.fixed_point_quantizer import gfixed | ||
| from tensorflow import keras | ||
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| from hls4ml.converters import convert_from_keras_model | ||
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| # tf.config.experimental_run_functions_eagerly(True) # noqa | ||
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| test_path = Path(__file__).parent | ||
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| def _run_synth_match_test(proxy: keras.Model, data, io_type: str, backend: str, dir: str, cond=None): | ||
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| output_dir = dir + '/hls4ml_prj' | ||
| hls_model = convert_from_keras_model( | ||
| proxy, | ||
| io_type=io_type, | ||
| output_dir=output_dir, | ||
| backend=backend, | ||
| hls_config={'Model': {'Precision': 'fixed<1,0>', 'ReuseFactor': 1}}, | ||
| ) | ||
| hls_model.compile() | ||
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| data_len = data.shape[0] if isinstance(data, np.ndarray) else data[0].shape[0] | ||
| # Multiple output case. Check each output separately | ||
| if len(proxy.outputs) > 1: # type: ignore | ||
| r_proxy: list[np.ndarray] = [x.numpy() for x in proxy(data)] # type: ignore | ||
| r_hls: list[np.ndarray] = hls_model.predict(data) # type: ignore | ||
| r_hls = [x.reshape(r_proxy[i].shape) for i, x in enumerate(r_hls)] | ||
| else: | ||
| r_proxy: list[np.ndarray] = [proxy(data).numpy()] # type: ignore | ||
| r_hls: list[np.ndarray] = [hls_model.predict(data).reshape(r_proxy[0].shape)] # type: ignore | ||
|
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| errors = [] | ||
| for i, (p, h) in enumerate(zip(r_proxy, r_hls)): | ||
| try: | ||
| if cond is None: | ||
| mismatch_ph = p != h | ||
| assert ( | ||
| np.sum(mismatch_ph) == 0 | ||
| ), f"Proxy-HLS4ML mismatch for out {i}: {np.sum(np.any(mismatch_ph, axis=1))} out of {data_len} samples are different. Sample: {p[mismatch_ph].ravel()[:5]} vs {h[mismatch_ph].ravel()[:5]}" # noqa: E501 | ||
| else: | ||
| cond(p, h) | ||
| except AssertionError as e: | ||
| errors.append(e) | ||
| if len(errors) > 0: | ||
| msgs = [str(e) for e in errors] | ||
| raise AssertionError('\n'.join(msgs)) | ||
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| def run_model_test( | ||
| model: keras.Model, cover_factor: float | None, data, io_type: str, backend: str, dir: str, aggressive: bool, cond=None | ||
| ): | ||
| data_len = data.shape[0] if isinstance(data, np.ndarray) else data[0].shape[0] | ||
| if cover_factor is not None: | ||
| trace_minmax(model, data, cover_factor=cover_factor, bsz=data_len) | ||
| proxy = to_proxy_model(model, aggressive=aggressive, unary_lut_max_table_size=4096) | ||
| _run_synth_match_test(proxy, data, io_type, backend, dir, cond=cond) | ||
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| def create_player_model(layer: str, rnd_strategy: str, io_type: str): | ||
| pa_config = get_default_paq_conf() | ||
| pa_config['rnd_strategy'] = rnd_strategy | ||
| pa_config['skip_dims'] = 'all' if io_type == 'io_stream' else 'batch' | ||
| set_default_paq_conf(pa_config) | ||
|
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| inp = keras.Input(shape=(15)) | ||
| if 'PConcatenate' in layer: | ||
| _inp = [HQuantize()(inp)] * 2 | ||
| out = eval(layer)(_inp) | ||
| out = HDense(15)(out) | ||
| return keras.Model(inp, out) | ||
| elif 'Signature' in layer: | ||
| _inp = eval(layer)(inp) | ||
| out = HDense(15)(_inp) | ||
| return keras.Model(inp, out) | ||
| elif 'Pool2D' in layer: | ||
| _inp = PReshape((3, 5, 1))(HQuantize()(inp)) | ||
| elif 'Pool1D' in layer: | ||
| _inp = PReshape((5, 3))(HQuantize()(inp)) | ||
| elif 'Dense' in layer or 'Activation' in layer: | ||
| _inp = HQuantize()(inp) | ||
| elif 'Flatten' in layer: | ||
| out = HQuantize()(inp) | ||
| out = PReshape((3, 5))(out) | ||
| out = HConv1D(2, 2)(out) | ||
| out = eval(layer)(out) | ||
| out = HDense(15)(out) | ||
| return keras.Model(inp, out) | ||
| else: | ||
| raise Exception(f'Please add test for {layer}') | ||
|
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| out = eval(layer)(_inp) | ||
| model = keras.Model(inp, out) | ||
|
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| for layer in model.layers: | ||
| # No weight bitwidths to randomize | ||
| # And activation bitwidths | ||
| if hasattr(layer, 'paq'): | ||
| fbw: tf.Variable = layer.paq.fbw | ||
| fbw.assign(tf.constant(np.random.uniform(4, 6, fbw.shape).astype(np.float32))) | ||
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| return model | ||
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| def get_data(shape: tuple[int, ...], v: float, max_scale: float): | ||
| rng = np.random.default_rng() | ||
| a1 = rng.uniform(-v, v, shape).astype(np.float32) | ||
| a2 = rng.uniform(0, max_scale, (1, shape[1])).astype(np.float32) | ||
| return (a1 * a2).astype(np.float32) | ||
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| @pytest.mark.parametrize( | ||
| 'layer', | ||
| [ | ||
| "PConcatenate()", | ||
| "PMaxPool1D(2, padding='same')", | ||
| "PMaxPool1D(4, padding='same')", | ||
| "PMaxPool2D((5,3), padding='same')", | ||
| "PMaxPool1D(2, padding='valid')", | ||
| "PMaxPool2D((2,3), padding='valid')", | ||
| "Signature(1,6,3)", | ||
| "PAvgPool1D(2, padding='same')", | ||
| "PAvgPool2D((1,2), padding='same')", | ||
| "PAvgPool2D((2,2), padding='same')", | ||
| "PAvgPool1D(2, padding='valid')", | ||
| "PAvgPool2D((1,2), padding='valid')", | ||
| "PAvgPool2D((2,2), padding='valid')", | ||
| "PFlatten()", | ||
| ], | ||
| ) | ||
| @pytest.mark.parametrize("N", [1000]) | ||
| @pytest.mark.parametrize("rnd_strategy", ['floor', 'standard_round']) | ||
| @pytest.mark.parametrize("io_type", ['io_parallel', 'io_stream']) | ||
| @pytest.mark.parametrize("cover_factor", [1.0]) | ||
| @pytest.mark.parametrize("aggressive", [True, False]) | ||
| @pytest.mark.parametrize("backend", ['vivado', 'vitis']) | ||
| def test_syn_players(layer, N: int, rnd_strategy: str, io_type: str, cover_factor: float, aggressive: bool, backend: str): | ||
| model = create_player_model(layer=layer, rnd_strategy=rnd_strategy, io_type=io_type) | ||
| data = get_data((N, 15), 7, 1) | ||
|
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| path = test_path / f'hls4mlprj_hgq_{layer}_{rnd_strategy}_{io_type}_{aggressive}_{backend}' | ||
|
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| if 'Signature' in layer: | ||
| q = gfixed(1, 6, 3) | ||
| data = q(data).numpy() | ||
| if "padding='same'" in layer and io_type == 'io_stream': | ||
| pytest.skip("io_stream does not support padding='same' for pools at the moment") | ||
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| run_model_test(model, cover_factor, data, io_type, backend, str(path), aggressive) |