pytorch/test/quantization/jit/test_deprecated_jit_quant.py

# Owner(s): ["oncall: quantization"]

import torch
from torch.testing._internal.common_quantization import skipIfNoFBGEMM
from torch.testing._internal.jit_utils import JitTestCase


class TestDeprecatedJitQuantized(JitTestCase):
    @skipIfNoFBGEMM
    def test_rnn_cell_quantized(self):
        d_in, d_hid = 2, 2

        for cell in [
            torch.nn.LSTMCell(d_in, d_hid).float(),
            torch.nn.GRUCell(d_in, d_hid).float(),
            torch.nn.RNNCell(d_in, d_hid).float(),
        ]:
            if isinstance(cell, torch.nn.LSTMCell):
                num_chunks = 4
            elif isinstance(cell, torch.nn.GRUCell):
                num_chunks = 3
            elif isinstance(cell, torch.nn.RNNCell):
                num_chunks = 1

            # Replace parameter values s.t. the range of values is exactly
            # 255, thus we will have 0 quantization error in the quantized
            # GEMM call. This i s for testing purposes.
            #
            # Note that the current implementation does not support
            # accumulation values outside of the range representable by a
            # 16 bit integer, instead resulting in a saturated value. We
            # must take care that in our test we do not end up with a dot
            # product that overflows the int16 range, e.g.
            # (255*127+255*127) = 64770. So, we hardcode the test values
            # here and ensure a mix of signedness.
            vals = [
                [100, -155],
                [100, -155],
                [-155, 100],
                [-155, 100],
                [100, -155],
                [-155, 100],
                [-155, 100],
                [100, -155],
            ]
            vals = vals[: d_hid * num_chunks]
            cell.weight_ih = torch.nn.Parameter(
                torch.tensor(vals, dtype=torch.float), requires_grad=False
            )
            cell.weight_hh = torch.nn.Parameter(
                torch.tensor(vals, dtype=torch.float), requires_grad=False
            )

            with self.assertRaisesRegex(
                RuntimeError,
                "quantize_rnn_cell_modules function is no longer supported",
            ):
                cell = torch.jit.quantized.quantize_rnn_cell_modules(cell)

    @skipIfNoFBGEMM
    def test_rnn_quantized(self):
        d_in, d_hid = 2, 2

        for cell in [
            torch.nn.LSTM(d_in, d_hid).float(),
            torch.nn.GRU(d_in, d_hid).float(),
        ]:
            # Replace parameter values s.t. the range of values is exactly
            # 255, thus we will have 0 quantization error in the quantized
            # GEMM call. This i s for testing purposes.
            #
            # Note that the current implementation does not support
            # accumulation values outside of the range representable by a
            # 16 bit integer, instead resulting in a saturated value. We
            # must take care that in our test we do not end up with a dot
            # product that overflows the int16 range, e.g.
            # (255*127+255*127) = 64770. So, we hardcode the test values
            # here and ensure a mix of signedness.
            vals = [
                [100, -155],
                [100, -155],
                [-155, 100],
                [-155, 100],
                [100, -155],
                [-155, 100],
                [-155, 100],
                [100, -155],
            ]
            if isinstance(cell, torch.nn.LSTM):
                num_chunks = 4
            elif isinstance(cell, torch.nn.GRU):
                num_chunks = 3
            vals = vals[: d_hid * num_chunks]
            cell.weight_ih_l0 = torch.nn.Parameter(
                torch.tensor(vals, dtype=torch.float), requires_grad=False
            )
            cell.weight_hh_l0 = torch.nn.Parameter(
                torch.tensor(vals, dtype=torch.float), requires_grad=False
            )

            with self.assertRaisesRegex(
                RuntimeError, "quantize_rnn_modules function is no longer supported"
            ):
                cell_int8 = torch.jit.quantized.quantize_rnn_modules(
                    cell, dtype=torch.int8
                )

            with self.assertRaisesRegex(
                RuntimeError, "quantize_rnn_modules function is no longer supported"
            ):
                cell_fp16 = torch.jit.quantized.quantize_rnn_modules(
                    cell, dtype=torch.float16
                )

    if "fbgemm" in torch.backends.quantized.supported_engines:

        def test_quantization_modules(self):
            K1, N1 = 2, 2

            class FooBar(torch.nn.Module):
                def __init__(self) -> None:
                    super().__init__()
                    self.linear1 = torch.nn.Linear(K1, N1).float()

                def forward(self, x):
                    x = self.linear1(x)
                    return x

            fb = FooBar()
            fb.linear1.weight = torch.nn.Parameter(
                torch.tensor([[-150, 100], [100, -150]], dtype=torch.float),
                requires_grad=False,
            )
            fb.linear1.bias = torch.nn.Parameter(
                torch.zeros_like(fb.linear1.bias), requires_grad=False
            )

            x = (torch.rand(1, K1).float() - 0.5) / 10.0
            value = torch.tensor([[100, -150]], dtype=torch.float)

            y_ref = fb(value)

            with self.assertRaisesRegex(
                RuntimeError, "quantize_linear_modules function is no longer supported"
            ):
                fb_int8 = torch.jit.quantized.quantize_linear_modules(fb)

            with self.assertRaisesRegex(
                RuntimeError, "quantize_linear_modules function is no longer supported"
            ):
                fb_fp16 = torch.jit.quantized.quantize_linear_modules(fb, torch.float16)

    @skipIfNoFBGEMM
    def test_erase_class_tensor_shapes(self):
        class Linear(torch.nn.Module):
            def __init__(self, in_features, out_features):
                super().__init__()
                qweight = torch._empty_affine_quantized(
                    [out_features, in_features],
                    scale=1,
                    zero_point=0,
                    dtype=torch.qint8,
                )
                self._packed_weight = torch.ops.quantized.linear_prepack(qweight)

            @torch.jit.export
            def __getstate__(self):
                return (
                    torch.ops.quantized.linear_unpack(self._packed_weight)[0],
                    self.training,
                )

            def forward(self):
                return self._packed_weight

            @torch.jit.export
            def __setstate__(self, state):
                self._packed_weight = torch.ops.quantized.linear_prepack(state[0])
                self.training = state[1]

            @property
            def weight(self):
                return torch.ops.quantized.linear_unpack(self._packed_weight)[0]

            @weight.setter
            def weight(self, w):
                self._packed_weight = torch.ops.quantized.linear_prepack(w)

        with torch._jit_internal._disable_emit_hooks():
            x = torch.jit.script(Linear(10, 10))
            torch._C._jit_pass_erase_shape_information(x.graph)


if __name__ == "__main__":
    raise RuntimeError(
        "This test file is not meant to be run directly, use:\n\n"
        "\tpython test/test_quantization.py TESTNAME\n\n"
        "instead."
    )