Move Tensor to nncf/__init__.py

nncf/__init__.py

@@ -18,6 +18,11 @@
 from nncf.common.strip import strip
 from nncf.config import NNCFConfig
 from nncf.data import Dataset
+from nncf.experimental.tensor.enums import TensorBackendType
+from nncf.experimental.tensor.enums import TensorDataType
+from nncf.experimental.tensor.enums import TensorDeviceType
+from nncf.experimental.tensor.functions import *
+from nncf.experimental.tensor.tensor import Tensor
 from nncf.parameters import DropType
 from nncf.parameters import ModelType
 from nncf.parameters import TargetDevice
@@ -49,7 +54,7 @@
             framework_present = True
     _AVAILABLE_FRAMEWORKS[fw_name] = framework_present
 
-if not any(_AVAILABLE_FRAMEWORKS.values()):
+if not sum(_AVAILABLE_FRAMEWORKS.values()):
     nncf_logger.error(
         "Neither PyTorch, TensorFlow, ONNX or OpenVINO Python packages have been found in your Python "
         "environment.\n"

nncf/experimental/common/tensor_statistics/statistical_functions.py

@@ -9,8 +9,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from nncf.experimental.tensor import Tensor
-from nncf.experimental.tensor import functions as fns
+import nncf
+from nncf import Tensor
 
 
 def mean_per_channel(x: Tensor, axis: int) -> Tensor:
@@ -22,9 +22,9 @@ def mean_per_channel(x: Tensor, axis: int) -> Tensor:
     :return: Reduced Tensor.
     """
     if len(x.shape) < 3:
-        return fns.mean(x, axis=0)
+        return nncf.mean(x, axis=0)
     pos_axis = axis + x.ndim if axis < 0 else axis
     if pos_axis < 0 or pos_axis >= x.ndim:
         raise ValueError(f"axis {axis} is out of bounds for array of dimension {x.ndim}")
     axis = tuple(i for i in range(x.ndim) if i != pos_axis)
-    return fns.mean(x, axis=axis)
+    return nncf.mean(x, axis=axis)

nncf/experimental/tensor/README.md

@@ -11,7 +11,7 @@ Common algorithms should use wrapped tensors and provide the unwrapped tensor to
 ### Initialization Tensor
 
 ```python
-from nncf.experimental.tensor import Tensor
+from nncf import Tensor
 
 import numpy as np
 numpy_array = np.array([1,2])
@@ -57,16 +57,16 @@ nncf_tensor.max()  # Tensor(2)
 All available functions you can found in [functions.py](functions.py).
 
 ```python
-from nncf.experimental.tensor import functions as fns
-fns.max(nncf_tensor)  # Tensor(2)
+import nncf
+nncf.max(nncf_tensor)  # Tensor(2)
 ```
 
 **NOTE** A function requires at least one positional argument, which is used to dispatch the function
 to the appropriate implementation depending on the type of argument.
 
 ```python
-fns.max(nncf_tensor)  # Correct
-fns.max(a=nncf_tensor)  # TypeError: wrapper requires at least 1 positional argument
+nncf.max(nncf_tensor)  # Correct
+nncf.max(a=nncf_tensor)  # TypeError: wrapper requires at least 1 positional argument
 ```
 
 ### Loop over Tensor

nncf/experimental/tensor/torch_functions.py

@@ -13,9 +13,9 @@
 
 import torch
 
-from nncf.experimental.tensor import TensorDataType
-from nncf.experimental.tensor import TensorDeviceType
 from nncf.experimental.tensor import functions as fns
+from nncf.experimental.tensor.enums import TensorDataType
+from nncf.experimental.tensor.enums import TensorDeviceType
 
 DTYPE_MAP = {
     TensorDataType.float16: torch.float16,

nncf/quantization/algorithms/fast_bias_correction/algorithm.py

@@ -12,7 +12,9 @@
 from math import inf
 from typing import Any, Dict, List, Optional, Tuple, TypeVar, Union
 
+import nncf
 from nncf import Dataset
+from nncf import Tensor
 from nncf.common.factory import EngineFactory
 from nncf.common.factory import ModelTransformerFactory
 from nncf.common.graph.graph import NNCFGraph
@@ -26,9 +28,7 @@
 from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer
 from nncf.common.utils.backend import BackendType
 from nncf.common.utils.backend import get_backend
-from nncf.experimental.common.tensor_statistics import statistical_functions as s_fns
-from nncf.experimental.tensor import Tensor
-from nncf.experimental.tensor import functions as fns
+from nncf.experimental.common.tensor_statistics.statistical_functions import mean_per_channel
 from nncf.quantization.algorithms.algorithm import Algorithm
 from nncf.quantization.algorithms.fast_bias_correction.backend import ALGO_BACKENDS
 
@@ -199,8 +199,8 @@ def _get_bias_shift_magnitude(current_bias_value: Tensor, updated_bias_value: Te
         :return: Magnitude between original and updated bias values.
         """
         bias_shift_magnitude = inf
-        if fns.count_nonzero(current_bias_value == 0) == 0:
-            bias_shift_magnitude = fns.max(fns.abs((updated_bias_value - current_bias_value) / current_bias_value))
+        if nncf.count_nonzero(current_bias_value == 0) == 0:
+            bias_shift_magnitude = nncf.max(nncf.abs((updated_bias_value - current_bias_value) / current_bias_value))
         return bias_shift_magnitude
 
     @staticmethod
@@ -318,8 +318,8 @@ def _get_bias_shift(
         engine = EngineFactory.create(model)
         raw_output = engine.infer(input_blob)
         q_outputs = self._backend_entity.process_model_output(raw_output, output_name)
-        q_outputs = s_fns.mean_per_channel(q_outputs, channel_axis)
-        bias_shift = fns.stack(output_fp) - q_outputs
+        q_outputs = mean_per_channel(q_outputs, channel_axis)
+        bias_shift = nncf.stack(output_fp) - q_outputs
         return bias_shift
 
     def get_statistic_points(self, model: TModel, graph: NNCFGraph) -> StatisticPointsContainer:

nncf/quantization/algorithms/fast_bias_correction/onnx_backend.py

@@ -14,12 +14,12 @@
 import numpy as np
 import onnx
 
+from nncf import Tensor
 from nncf.common.graph import NNCFGraph
 from nncf.common.graph import NNCFNode
 from nncf.common.graph.transformations.commands import TargetType
 from nncf.common.tensor_statistics.collectors import ReductionShape
 from nncf.common.utils.backend import BackendType
-from nncf.experimental.tensor import Tensor
 from nncf.onnx.graph.node_utils import get_bias_value
 from nncf.onnx.graph.node_utils import is_any_weight_quantized
 from nncf.onnx.graph.node_utils import is_node_with_bias

nncf/quantization/algorithms/fast_bias_correction/openvino_backend.py

@@ -14,13 +14,13 @@
 import numpy as np
 import openvino.runtime as ov
 
+from nncf import Tensor
 from nncf.common.graph import NNCFGraph
 from nncf.common.graph import NNCFNode
 from nncf.common.graph.transformations.commands import TargetType
 from nncf.common.tensor_statistics.collectors import ReductionShape
 from nncf.common.utils.backend import BackendType
 from nncf.experimental.common.tensor_statistics.collectors import TensorCollector
-from nncf.experimental.tensor import Tensor
 from nncf.openvino.graph.metatypes.groups import FAKE_QUANTIZE_OPERATIONS
 from nncf.openvino.graph.node_utils import get_bias_value
 from nncf.openvino.graph.node_utils import is_node_with_bias

nncf/quantization/algorithms/fast_bias_correction/torch_backend.py

@@ -14,13 +14,13 @@
 import numpy as np
 import torch
 
+from nncf import Tensor
 from nncf.common.graph import NNCFGraph
 from nncf.common.graph import NNCFNode
 from nncf.common.graph.definitions import NNCFGraphNodeType
 from nncf.common.graph.transformations.commands import TargetType
 from nncf.common.tensor_statistics.collectors import ReductionShape
 from nncf.common.utils.backend import BackendType
-from nncf.experimental.tensor import Tensor
 from nncf.quantization.algorithms.fast_bias_correction.backend import ALGO_BACKENDS
 from nncf.quantization.algorithms.fast_bias_correction.backend import FastBiasCorrectionAlgoBackend
 from nncf.torch.graph.transformations.command_creation import create_bias_correction_command

nncf/quantization/fake_quantize.py

@@ -14,16 +14,16 @@
 
 import numpy as np
 
+import nncf
+from nncf import Tensor
+from nncf import TensorDataType
 from nncf.common.quantization.quantizers import calculate_asymmetric_level_ranges
 from nncf.common.quantization.quantizers import calculate_symmetric_level_ranges
 from nncf.common.quantization.quantizers import get_num_levels
 from nncf.common.quantization.structs import QuantizationMode
 from nncf.common.quantization.structs import QuantizerConfig
 from nncf.common.quantization.structs import QuantizerGroup
 from nncf.common.tensor_statistics.statistics import MinMaxTensorStatistic
-from nncf.experimental.tensor import Tensor
-from nncf.experimental.tensor import TensorDataType
-from nncf.experimental.tensor import functions as fns
 
 
 @dataclass
@@ -53,9 +53,9 @@ def fix_zero_filters_symmetric(max_values: Tensor, eps: float = 0.01) -> Tensor:
     :param eps: Correction coefficient.
     :return: Fixed the high quant number.
     """
-    max_range = fns.max(max_values)
-    lower_threshold = fns.maximum(max_range * eps, 8e-5)
-    return fns.maximum(lower_threshold, max_values)
+    max_range = nncf.max(max_values)
+    lower_threshold = nncf.maximum(max_range * eps, 8e-5)
+    return nncf.maximum(lower_threshold, max_values)
 
 
 def fix_zero_filters_asymmetric(min_values: Tensor, max_values: Tensor, eps: float = 1e-8) -> Tuple[Tensor, Tensor]:
@@ -71,7 +71,9 @@ def fix_zero_filters_asymmetric(min_values: Tensor, max_values: Tensor, eps: flo
     """
     ranges = max_values - min_values
     min_correction = 8e-4
-    corrections = fns.where(ranges > min_correction, (fns.maximum(eps * ranges, ranges) - ranges) * 0.5, min_correction)
+    corrections = nncf.where(
+        ranges > min_correction, (nncf.maximum(eps * ranges, ranges) - ranges) * 0.5, min_correction
+    )
 
     level_low = min_values - corrections
     level_high = max_values + corrections
@@ -99,21 +101,21 @@ def tune_range(
     if unify_zp:
         scale = (right_border - left_border) / level_high
         zero_point = -left_border / scale
-        avg_zpts = fns.round(fns.mean(zero_point))
-        qval = fns.ones_like(left_border) * avg_zpts
+        avg_zpts = nncf.round(nncf.mean(zero_point))
+        qval = nncf.ones_like(left_border) * avg_zpts
     else:
         s = level_high / (right_border - left_border)
         fval = -left_border * s
-        qval = fns.round(fval)
+        qval = nncf.round(fval)
 
-    ra = fns.where(qval < level_high, qval / (qval - level_high) * right_border, left_border)
-    rb = fns.where(qval > 0.0, (qval - level_high) / qval * left_border, right_border)
+    ra = nncf.where(qval < level_high, qval / (qval - level_high) * right_border, left_border)
+    rb = nncf.where(qval > 0.0, (qval - level_high) / qval * left_border, right_border)
 
     range_a = right_border - ra
     range_b = rb - left_border
 
-    mask = fns.where(range_a > range_b, 1.0, 0.0)
-    inv_mask = fns.abs(1.0 - mask)
+    mask = nncf.where(range_a > range_b, 1.0, 0.0)
+    inv_mask = nncf.abs(1.0 - mask)
 
     ra = mask * ra + inv_mask * left_border
     rb = inv_mask * rb + mask * right_border
@@ -145,8 +147,8 @@ def symmetric_range(
     else:
         signed = quantizer_config.signedness_to_force is True
         level_low = (
-            fns.zeros_like(level_high)
-            if fns.all(min_values >= 0) and not signed
+            nncf.zeros_like(level_high)
+            if nncf.all(min_values >= 0) and not signed
             else -level_high * levels / (levels - 2)
         )
 
@@ -175,8 +177,8 @@ def asymmetric_range(
         level_high - the high quant number
     """
     level_low, level_high = fix_zero_filters_asymmetric(min_values, max_values)
-    level_low = fns.where(level_low < 0.0, level_low, 0.0)
-    level_high = fns.where(level_high > 0.0, level_high, 0.0)
+    level_low = nncf.where(level_low < 0.0, level_low, 0.0)
+    level_high = nncf.where(level_high > 0.0, level_high, 0.0)
 
     if unify_zp and q_group == QuantizerGroup.ACTIVATIONS:
         raise NotImplementedError("Unified zero point is not supported for activations.")
@@ -239,8 +241,8 @@ def calculate_quantizer_parameters(
             input_low, input_high = asymmetric_range(min_values, max_values, quantizer_config, quant_group)
 
     if not quantizer_config.per_channel:
-        input_low = fns.squeeze(input_low)
-        input_high = fns.squeeze(input_high)
+        input_low = nncf.squeeze(input_low)
+        input_high = nncf.squeeze(input_high)
 
     output_low, output_high = input_low, input_high
     return FakeQuantizeParameters(input_low, input_high, output_low, output_high, levels)

tests/onnx/quantization/common.py

@@ -16,7 +16,7 @@
 import onnx
 
 from nncf import Dataset
-from nncf.experimental.tensor import Tensor
+from nncf import Tensor
 from nncf.onnx.graph.nncf_graph_builder import GraphConverter
 from nncf.onnx.graph.onnx_graph import ONNXGraph
 from nncf.onnx.statistics.statistics import ONNXMinMaxTensorStatistic

tests/post_training/test_templates/test_calculate_quantizer_parameters.py

@@ -16,10 +16,10 @@
 import numpy as np
 import pytest
 
+import nncf
 from nncf.common.quantization.structs import QuantizationMode
 from nncf.common.quantization.structs import QuantizerConfig
 from nncf.common.quantization.structs import QuantizerGroup
-from nncf.experimental.tensor import functions as fns
 from nncf.quantization.fake_quantize import FakeQuantizeParameters
 from nncf.quantization.fake_quantize import calculate_quantizer_parameters
 from tests.post_training.conftest import FQ_CALCULATED_PARAMETERS_PATH
@@ -33,10 +33,10 @@ def compare_fq_parameters(ref_params, params):
     assert ref_params.input_high.shape == params.input_high.shape
     assert ref_params.output_low.shape == params.output_low.shape
     assert ref_params.output_high.shape == params.output_high.shape
-    assert fns.allclose(ref_params.input_low, params.input_low)
-    assert fns.allclose(ref_params.input_high, params.input_high)
-    assert fns.allclose(ref_params.output_low, params.output_low)
-    assert fns.allclose(ref_params.output_high, params.output_high)
+    assert nncf.allclose(ref_params.input_low, params.input_low)
+    assert nncf.allclose(ref_params.input_high, params.input_high)
+    assert nncf.allclose(ref_params.output_low, params.output_low)
+    assert nncf.allclose(ref_params.output_high, params.output_high)
 
 
 def get_test_reference_key(q_group, q_config, narrow_range, hf_range):