66e1af18b5
* [Pt FE]: aten::embedding_bag * Update src/frontends/pytorch/src/op_table.cpp Co-authored-by: Maxim Vafin <maxim.vafin@intel.com> --------- Co-authored-by: Maxim Vafin <maxim.vafin@intel.com>
92 lines
4.1 KiB
Python
92 lines
4.1 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import pytest
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from pytorch_layer_test_class import PytorchLayerTest
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class TestEmbeddingBag1dOffsets(PytorchLayerTest):
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def _prepare_input(self, indicies_dtype, per_sample_weights=False):
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import numpy as np
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indices = np.array([2, 2, 2, 2, 4, 3, 2, 9]).astype(indicies_dtype)
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weights = np.random.randn(10, 10).astype(np.float32)
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offsets = np.array([0, 4]).astype(indicies_dtype)
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if per_sample_weights:
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per_sample_weights = np.random.randn(
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*indices.shape).astype(np.float32)
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return (indices, weights, offsets, per_sample_weights)
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return (indices, weights, offsets)
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def create_model(self, per_sample_weights):
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import torch
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import torch.nn.functional as F
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class aten_embedding_bag(torch.nn.Module):
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def __init__(self, per_sample_weights=False) -> None:
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super().__init__()
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if per_sample_weights:
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self.forward = self.forward_offsets_per_sample_weights
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def forward(self, indicies, weight, offsets):
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return F.embedding_bag(indicies, weight, offsets, mode="sum")
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def forward_offsets_per_sample_weights(self, indicies, weight, offsets, per_sample_wights):
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return F.embedding_bag(indicies, weight, offsets, mode="sum", per_sample_weights=per_sample_wights)
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ref_net = None
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return aten_embedding_bag(per_sample_weights), ref_net, "aten::embedding_bag"
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@pytest.mark.nightly
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@pytest.mark.precommit
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@pytest.mark.parametrize("indicies_dtype", ["int", "int32"])
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@pytest.mark.parametrize("per_sample_weights", [True, False])
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def test_embedding_bag(self, ie_device, precision, ir_version, indicies_dtype, per_sample_weights):
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self._test(*self.create_model(per_sample_weights), ie_device, precision, ir_version,
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kwargs_to_prepare_input={"indicies_dtype": indicies_dtype, "per_sample_weights": per_sample_weights},
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trace_model=True, dynamic_shapes=not per_sample_weights)
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class TestEmbeddingBag2d(PytorchLayerTest):
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def _prepare_input(self, indicies_size, indicies_dtype, per_sample_weights):
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import numpy as np
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indices = np.random.randint(
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0, 9, size=indicies_size).astype(indicies_dtype)
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weights = np.random.randn(10, 10).astype(np.float32)
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if per_sample_weights:
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per_sample_weights = np.random.randn(
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*indices.shape).astype(np.float32)
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return (indices, weights, per_sample_weights)
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return (indices, weights)
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def create_model(self, per_sample_weights):
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import torch
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import torch.nn.functional as F
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class aten_embedding_bag(torch.nn.Module):
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def __init__(self, per_sample_weights=False) -> None:
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super().__init__()
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if per_sample_weights:
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self.forward = self.forward_per_sample_weights
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def forward(self, indicies, weight):
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return F.embedding_bag(indicies, weight, mode="sum")
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def forward_per_sample_weights(self, indicies, weight, per_sample_wights):
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return F.embedding_bag(indicies, weight, mode="sum", per_sample_weights=per_sample_wights)
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ref_net = None
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return aten_embedding_bag(per_sample_weights), ref_net, "aten::embedding_bag"
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@pytest.mark.nightly
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@pytest.mark.precommit
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@pytest.mark.parametrize("indicies_size", [[1, 1], [2, 5], [3, 10], [4, 7]])
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@pytest.mark.parametrize("indicies_dtype", ["int", "int32"])
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@pytest.mark.parametrize("per_sample_weights", [True, False])
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def test_embedding_bag(self, ie_device, precision, ir_version, indicies_dtype, indicies_size, per_sample_weights):
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self._test(*self.create_model(per_sample_weights), ie_device, precision, ir_version,
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kwargs_to_prepare_input={"indicies_size": indicies_size, "indicies_dtype": indicies_dtype, "per_sample_weights": per_sample_weights},
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trace_model=True, dynamic_shapes=not per_sample_weights)
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