test_lookup.py

# Copyright (c) 2021, Xilinx
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import numpy as np
import onnx  # noqa
import os
import torch
from brevitas.export import FINNManager
from torch import nn

from finn.core.datatype import DataType
from finn.core.modelwrapper import ModelWrapper
from finn.core.onnx_exec import execute_onnx
from finn.transformation.infer_datatypes import InferDataTypes
from finn.transformation.infer_shapes import InferShapes
from finn.util.basic import gen_finn_dt_tensor

tmpdir = os.environ["FINN_BUILD_DIR"]


def make_lookup_model(embeddings, ishape, idt, edt):
    num_embeddings, embedding_dim = embeddings.shape

    class LookupModel(nn.Module):
        def __init__(self, num_embeddings, embedding_dim):
            super().__init__()
            self.lookup = nn.Embedding(
                num_embeddings=num_embeddings, embedding_dim=embedding_dim
            )

        def forward(self, x):
            x = self.lookup(x)
            return x

    torch_model = LookupModel(num_embeddings, embedding_dim)
    input_t = torch.zeros(ishape, dtype=torch.int64)
    ret = FINNManager.export(torch_model, input_t=input_t, opset_version=11)
    model = ModelWrapper(ret)
    iname = model.graph.input[0].name
    ename = model.graph.node[0].input[0]
    model.set_tensor_datatype(iname, idt)
    eshape = model.get_tensor_shape(ename)
    assert tuple(eshape) == embeddings.shape
    model.set_initializer(ename, embeddings)
    model.set_tensor_datatype(ename, edt)
    model = model.transform(InferShapes())
    model = model.transform(InferDataTypes())
    return model


def test_lookup_export():
    export_path = tmpdir + "/test_lookup_export.onnx"
    ishape = (1, 10)
    idt = DataType["UINT8"]
    edt = DataType["FIXED<8,2>"]
    num_embeddings = 2 ** idt.bitwidth()
    embedding_dim = 2
    eshape = (num_embeddings, embedding_dim)
    exp_oshape = tuple(list(ishape) + [embedding_dim])
    embeddings = gen_finn_dt_tensor(edt, eshape)
    model = make_lookup_model(embeddings, ishape, idt, edt)
    model.save(export_path)
    assert len(model.graph.node) == 1
    assert model.graph.node[0].op_type == "Gather"
    iname = model.graph.input[0].name
    ename = model.graph.node[0].input[0]
    oname = model.graph.output[0].name
    assert model.get_tensor_datatype(iname) == idt
    assert model.get_tensor_datatype(ename) == edt
    assert model.get_tensor_datatype(oname) == edt
    assert tuple(model.get_tensor_shape(ename)) == eshape
    assert tuple(model.get_tensor_shape(oname)) == exp_oshape
    assert (model.get_initializer(ename) == embeddings).all()
    itensor = gen_finn_dt_tensor(idt, ishape).astype(np.int64)
    ret = execute_onnx(model, {iname: itensor})
    exp_out = np.take(embeddings, itensor, axis=0)
    assert (exp_out == ret[oname]).all()