Rollback ONNX/PT changes

nncf/onnx/graph/metatypes/onnx_metatypes.py

@@ -677,18 +677,6 @@ class ONNXErfMetatype(ONNXOpMetatype):
     op_names = ["Erf"]
 
 
-@ONNX_OPERATION_METATYPES.register()
-class ONNXCosMetatype(ONNXOpMetatype):
-    name = "CosOp"
-    op_names = ["Cos"]
-
-
-@ONNX_OPERATION_METATYPES.register()
-class ONNXSinMetatype(ONNXOpMetatype):
-    name = "SinOp"
-    op_names = ["Sin"]
-
-
 def get_operator_metatypes() -> List[Type[OperatorMetatype]]:
     """
     Returns a list of the operator metatypes.

nncf/onnx/quantization/ignored_patterns.py

@@ -157,25 +157,3 @@ def create_se_block() -> GraphPattern:
                 multiply_node = node_id
         pattern.add_edge(non_pattern_node, multiply_node)
     return pattern
-
-
-@ONNX_IGNORED_PATTERNS.register(IgnoredPatternNames.ROPE)
-def create_rope() -> GraphPattern:
-    pattern = GraphPattern()
-    matmul_node = pattern.add_node(
-        **{GraphPattern.LABEL_ATTR: "MATMUL", GraphPattern.METATYPE_ATTR: om.ONNXMatMulMetatype}
-    )
-    transpose_node = pattern.add_node(
-        **{GraphPattern.LABEL_ATTR: "TRANSPOSE", GraphPattern.METATYPE_ATTR: om.ONNXTransposeMetatype}
-    )
-    concat_node = pattern.add_node(
-        **{GraphPattern.LABEL_ATTR: "CONCAT", GraphPattern.METATYPE_ATTR: om.ONNXConcatMetatype}
-    )
-    cos_node = pattern.add_node(**{GraphPattern.LABEL_ATTR: "COS", GraphPattern.METATYPE_ATTR: om.ONNXCosMetatype})
-    sin_node = pattern.add_node(**{GraphPattern.LABEL_ATTR: "SIN", GraphPattern.METATYPE_ATTR: om.ONNXSinMetatype})
-
-    pattern.add_edge(matmul_node, transpose_node)
-    pattern.add_edge(transpose_node, concat_node)
-    pattern.add_edge(concat_node, cos_node)
-    pattern.add_edge(concat_node, sin_node)
-    return pattern

nncf/torch/graph/operator_metatypes.py

@@ -1110,18 +1110,6 @@ class PTScaledDotProductAttentionMetatype(PTOperatorMetatype):
     target_input_ports = [0, 1]
 
 
-@PT_OPERATOR_METATYPES.register()
-class PTCosMetatype(PTOperatorMetatype):
-    name = "CosOp"
-    module_to_function_names = {NamespaceTarget.TORCH: ["cos"]}
-
-
-@PT_OPERATOR_METATYPES.register()
-class PTSinMetatype(PTOperatorMetatype):
-    name = "SinOp"
-    module_to_function_names = {NamespaceTarget.TORCH: ["sin"]}
-
-
 def get_operator_metatypes() -> List[Type[OperatorMetatype]]:
     """
     Returns a list of the operator metatypes.

nncf/torch/quantization/ignored_patterns.py

@@ -227,23 +227,3 @@ def get_se_block_with_bias_and_reshape() -> GraphPattern:
     main_pattern.add_pattern_alternative(get_se_block_with_reshape())
     main_pattern.add_pattern_alternative(get_se_block_with_bias_and_reshape())
     return main_pattern
-
-
-@PT_IGNORED_PATTERNS.register(IgnoredPatternNames.ROPE)
-def create_rope() -> GraphPattern:
-    pattern = GraphPattern()
-    matmul_node = pattern.add_node(
-        **{GraphPattern.LABEL_ATTR: "MATMUL", GraphPattern.METATYPE_ATTR: om.PTMatMulMetatype}
-    )
-    transpose_node = pattern.add_node(
-        **{GraphPattern.LABEL_ATTR: "TRANSPOSE", GraphPattern.METATYPE_ATTR: om.PTTransposeMetatype}
-    )
-    concat_node = pattern.add_node(**{GraphPattern.LABEL_ATTR: "CONCAT", GraphPattern.METATYPE_ATTR: om.PTCatMetatype})
-    cos_node = pattern.add_node(**{GraphPattern.LABEL_ATTR: "COS", GraphPattern.METATYPE_ATTR: om.PTCosMetatype})
-    sin_node = pattern.add_node(**{GraphPattern.LABEL_ATTR: "SIN", GraphPattern.METATYPE_ATTR: om.PTSinMetatype})
-
-    pattern.add_edge(matmul_node, transpose_node)
-    pattern.add_edge(transpose_node, concat_node)
-    pattern.add_edge(concat_node, cos_node)
-    pattern.add_edge(concat_node, sin_node)
-    return pattern

tests/cross_fw/test_templates/helpers.py

@@ -466,22 +466,3 @@ def __init__(self):
 
     def forward(self, x):
         return self.conv(x)
-
-
-class RoPEModel(nn.Module):
-    INPUT_SIZE = [1, 10]
-
-    def __init__(self):
-        super().__init__()
-        with set_torch_seed():
-            self.data = torch.randn([5])
-
-    def forward(self, x):
-        x = torch.unsqueeze(x, dim=0)
-        reshape = torch.reshape(self.data, [1, 5, 1])
-        x = torch.matmul(reshape, x)
-        x = torch.transpose(x, 2, 1)
-        x = torch.cat([x], dim=2)
-        x1 = x.sin()
-        x2 = x.cos()
-        return x1, x2

tests/onnx/models.py

@@ -1757,126 +1757,3 @@ def __init__(self):
         model = onnx.helper.make_model(graph_def, opset_imports=[op])
         onnx.checker.check_model(model)
         super().__init__(model, [input_shape], "unified_embedding_model.dot")
-
-
-class RoPEModel(ONNXReferenceModel):
-    def __init__(self):
-        rng = np.random.default_rng(seed=0)
-
-        input_shape = [1, 10]
-        input_name = "model_in"
-        model_in = onnx.helper.make_tensor_value_info(input_name, onnx.TensorProto.INT64, input_shape)
-
-        output_shape = [1, 5, 10]
-        cos_out_name = "cos_out"
-        cos_out = onnx.helper.make_tensor_value_info(cos_out_name, onnx.TensorProto.FLOAT, output_shape)
-        sin_out_name = "sin_out"
-        sin_out = onnx.helper.make_tensor_value_info(sin_out_name, onnx.TensorProto.FLOAT, output_shape)
-
-        unsqueeze_out_name = "un_out"
-        unsqueeze_tensor_name = "un_tensor"
-        unsqueeze_tensor = create_initializer_tensor(
-            name=unsqueeze_tensor_name, tensor_array=np.int64([2]), data_type=onnx.TensorProto.INT64
-        )
-        unsqueeze_node = onnx.helper.make_node(
-            name="unsqueeze",
-            op_type="Unsqueeze",
-            inputs=[input_name, unsqueeze_tensor_name],
-            outputs=[unsqueeze_out_name],
-        )
-
-        cast_out_name = "cast_out"
-        cast_node = onnx.helper.make_node(
-            name="cast",
-            op_type="Cast",
-            to=onnx.TensorProto.FLOAT,
-            inputs=[unsqueeze_out_name],
-            outputs=[cast_out_name],
-        )
-
-        reshape_shape_name = "re_shape"
-        reshape_shape = create_initializer_tensor(
-            name=reshape_shape_name,
-            tensor_array=np.array([1, 5]).astype(np.int64),
-            data_type=onnx.TensorProto.INT64,
-        )
-        reshape_tensor_name = "re_tensor"
-        reshape_tensor = create_initializer_tensor(
-            name=reshape_tensor_name,
-            tensor_array=rng.uniform(0, 1, (5)).astype(np.float32),
-            data_type=onnx.TensorProto.FLOAT,
-        )
-        reshape_out_name = "re_out"
-        reshape_node = onnx.helper.make_node(
-            name="reshape",
-            op_type="Reshape",
-            inputs=[reshape_tensor_name, reshape_shape_name],
-            outputs=[reshape_out_name],
-        )
-
-        matmul_out_name = "mm_out"
-        matmul_node = onnx.helper.make_node(
-            name="matmul",
-            op_type="MatMul",
-            inputs=[cast_out_name, reshape_out_name],
-            outputs=[matmul_out_name],
-        )
-
-        transpose_out_name = "trans_out"
-        transpose_node = onnx.helper.make_node(
-            name="transpose",
-            op_type="Transpose",
-            inputs=[matmul_out_name],
-            outputs=[transpose_out_name],
-            perm=[0, 2, 1],
-        )
-
-        concat_out_name = "concat_out"
-        concat_node = onnx.helper.make_node(
-            name="concat",
-            op_type="Concat",
-            inputs=[transpose_out_name],
-            outputs=[concat_out_name],
-            axis=-1,
-        )
-
-        sin_node = onnx.helper.make_node(
-            name="sin",
-            op_type="Sin",
-            inputs=[concat_out_name],
-            outputs=[sin_out_name],
-        )
-
-        cos_node = onnx.helper.make_node(
-            name="cos",
-            op_type="Cos",
-            inputs=[concat_out_name],
-            outputs=[cos_out_name],
-        )
-
-        graph_def = onnx.helper.make_graph(
-            nodes=[
-                unsqueeze_node,
-                cast_node,
-                reshape_node,
-                matmul_node,
-                transpose_node,
-                concat_node,
-                sin_node,
-                cos_node,
-            ],
-            name="RoPEModel",
-            inputs=[model_in],
-            outputs=[sin_out, cos_out],
-            initializer=[
-                unsqueeze_tensor,
-                reshape_tensor,
-                reshape_shape,
-            ],
-        )
-
-        op = onnx.OperatorSetIdProto()
-        op.version = OPSET_VERSION
-        model = onnx.helper.make_model(graph_def, opset_imports=[op])
-        onnx.checker.check_model(model)
-        super().__init__(model, [input_shape], "rope_model.dot")

tests/onnx/quantization/test_graphs.py

@@ -11,9 +11,7 @@
 
 import pytest
 
-from nncf.parameters import ModelType
 from tests.onnx.models import ALL_SYNTHETIC_MODELS
-from tests.onnx.models import RoPEModel
 from tests.onnx.quantization.common import compare_nncf_graph
 from tests.onnx.quantization.common import min_max_quantize_model
 from tests.onnx.quantization.common import mock_collect_statistics
@@ -25,13 +23,3 @@ def test_synthetic_models_graph(model_cls_to_test, mocker):
     model_to_test = model_cls_to_test()
     quantized_model = min_max_quantize_model(model_to_test.onnx_model)
     compare_nncf_graph(quantized_model, "synthetic/" + model_to_test.path_ref_graph)
-
-
-@pytest.mark.parametrize("model_cls_to_test", [RoPEModel])
-def test_synthetic_models_graph_transformer(model_cls_to_test, mocker):
-    mock_collect_statistics(mocker)
-    model_to_test = model_cls_to_test()
-    quantized_model = min_max_quantize_model(
-        model_to_test.onnx_model, quantization_params={"model_type": ModelType.TRANSFORMER}
-    )
-    compare_nncf_graph(quantized_model, "synthetic/" + model_to_test.path_ref_graph)

tests/onnx/test_pattern_manager.py

@@ -53,6 +53,7 @@
 IGNORING_IGNORED_PATTERN_REASONS = {
     IgnoredPatternNames.FC_BN_HSWISH_ACTIVATION: "Not relevant for ONNX.",
     IgnoredPatternNames.EQUAL_LOGICALNOT: "Not relevant for ONNX.",
+    IgnoredPatternNames.ROPE: "Not relevant for ONNX.",
 }
 
 

tests/torch/ptq/test_graphs.py

@@ -16,15 +16,13 @@
 import torch
 
 from nncf import Dataset
-from nncf.parameters import ModelType
 from nncf.parameters import TargetDevice
 from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters
 from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
 from nncf.torch import wrap_model
 from nncf.torch.layers import NNCF_RNN
 from nncf.torch.layers import LSTMCellNNCF
 from tests.cross_fw.test_templates.helpers import EmbeddingModel
-from tests.cross_fw.test_templates.helpers import RoPEModel
 from tests.cross_fw.test_templates.helpers import ScaledDotProductAttentionModel
 from tests.torch import test_models
 from tests.torch.quantization.test_algo_quantization import SharedLayersModel
@@ -52,7 +50,6 @@ def get_model_name(description):
 
 TEST_MODELS_DESC = [
     (ModelDesc("embedding_model", EmbeddingModel, [1, 10]), {}),
-    (ModelDesc("rope_model", RoPEModel, [1, 10]), {"model_type": ModelType.TRANSFORMER}),
     (
         ModelDesc(
             "scaled_dot_product_attention_model",

tests/torch/test_pattern_manager.py

@@ -74,6 +74,7 @@
 IGNORING_IGNORED_PATTERN_REASONS = {
     IgnoredPatternNames.FC_BN_HSWISH_ACTIVATION: "Not relevant for Torch.",
     IgnoredPatternNames.EQUAL_LOGICALNOT: "Not relevant for Torch.",
+    IgnoredPatternNames.ROPE: "Not relevant for Torch.",
 }