Update docsrc (#1339)

* Update docsrc.

* Fix comments that were parsed wrongly in docs.

---------

Co-authored-by: reuvenp <[email protected]>

docsrc/source/api/api_docs/classes/GradientPTQConfig.rst

@@ -8,7 +8,7 @@ GradientPTQConfig Class
 =================================
 
 
-**The following API can be used to create a GradientPTQConfig instance which can be used for post training quantization using knowledge distillation from a teacher (float Keras model) to a student (the quantized Keras model)**
+**The following API can be used to create a GradientPTQConfig instance which can be used for post training quantization using knowledge distillation from a teacher (float model) to a student (the quantized model)**
 
 .. autoclass:: model_compression_toolkit.gptq.GradientPTQConfig
     :members:
@@ -30,3 +30,22 @@ RoundingType
 
 .. autoclass:: model_compression_toolkit.gptq.RoundingType
     :members:
+
+
+=====================================
+GradualActivationQuantizationConfig
+=====================================
+
+**The following API can be used to configure the gradual activation quantization when using GPTQ.**
+
+.. autoclass:: model_compression_toolkit.gptq.GradualActivationQuantizationConfig
+    :members:
+
+
+=====================================
+QFractionLinearAnnealingConfig
+=====================================
+
+.. autoclass:: model_compression_toolkit.gptq.QFractionLinearAnnealingConfig
+    :members:
+

docsrc/source/api/api_docs/modules/target_platform_capabilities.rst

@@ -27,7 +27,7 @@ Models for IMX500, TFLite and qnnpack can be observed `here <https://github.com/
 The object MCT should get called TargetPlatformCapabilities (or shortly TPC).
 This diagram demonstrates the main components:
 
-.. image:: ../../../../images/tpc.jpg
+.. image:: ../../../../images/tpc_diagram.png
   :scale: 80%
 
 Now, we will detail about the different components.

docsrc/source/diagrams/tpc.mermaid

@@ -0,0 +1,46 @@
+flowchart TB
+    subgraph TPC["Target Platform Capabilities"]
+        subgraph QCO["Quantization Config Options"]
+            Default["Default QCO (8-bit)"]
+            Mixed["Mixed Precision QCO (8, 4, 2 bits)"]
+            NoQuant["No Quantization QCO"]
+
+            subgraph OQC["Op Quantization Config"]
+                OQC1["8-bit"]
+                OQC2["4-bit"]
+                OQC3["2-bit"]
+                AQC["AttributeQuantizationConfig:<br>Kernel, Bias"]
+
+                OQC1 -->|contains| AQC
+                OQC2 -->|contains| AQC
+                OQC3 -->|contains| AQC
+            end
+
+            Mixed -->|contains| OQC1
+            Mixed -->|contains| OQC2
+            Mixed -->|contains| OQC3
+        end
+
+        subgraph OPS["Operators Sets"]
+            Conv["Conv, Conv Transpose,<br>Depthwise Conv"]
+            Act["ReLU, ReLU6,<br>Leaky ReLU, etc."]
+            NoQuantOps["Dropout, Flatten,<br>Reshape, etc."]
+        end
+
+        subgraph FP["Fusing Patterns"]
+            FP1["Conv + Activation"]
+        end
+
+        Mixed -->|attached to| Conv
+        Default -->|attached to| Act
+        NoQuant -->|attached to| NoQuantOps
+
+        FP1 -.-> Conv
+        FP1 -.-> Act
+    end
+
+    style TPC fill:#e6f3ff,stroke:#333
+    style QCO fill:#e6ffe6,stroke:#333
+    style OQC fill:#fff9e6,stroke:#333
+    style OPS fill:#ffe6e6,stroke:#333
+    style FP fill:#ffe6f0,stroke:#333

model_compression_toolkit/core/common/quantization/quantization_config.py

@@ -78,9 +78,6 @@ class QuantizationConfig:
         >>> qc = mct.core.QuantizationConfig(activation_error_method=mct.core.QuantizationErrorMethod.NOCLIPPING, weights_error_method=mct.core.QuantizationErrorMethod.MSE, relu_bound_to_power_of_2=True, weights_bias_correction=True)
 
 
-        The QuantizationConfig instance can then be used in the quantization workflow,
-        such as with Keras in the function: :func:~model_compression_toolkit.ptq.keras_post_training_quantization`.
-
     """
 
     activation_error_method: QuantizationErrorMethod = QuantizationErrorMethod.MSE

model_compression_toolkit/gptq/pytorch/quantization_facade.py

@@ -77,6 +77,7 @@ def get_pytorch_gptq_config(n_epochs: int,
             regularization_factor (float): A floating point number that defines the regularization factor.
             hessian_batch_size (int): Batch size for Hessian computation in Hessian-based weights GPTQ.
             use_hessian_sample_attention (bool): whether to use Sample-Layer Attention score for weighted loss.
+            gradual_activation_quantization (bool, GradualActivationQuantizationConfig): If False, GradualActivationQuantization is disabled. If True, GradualActivationQuantization is enabled with the default settings. GradualActivationQuantizationConfig object can be passed to use non-default settings.
 
         returns:
             a GradientPTQConfig object to use when fine-tuning the quantized model using gptq.

model_compression_toolkit/gptq/pytorch/quantizer/quantization_builder.py

@@ -16,12 +16,9 @@
 
 from model_compression_toolkit.gptq import GradientPTQConfig
 from model_compression_toolkit.core import common
-from model_compression_toolkit.exporter.model_wrapper.pytorch.builder.node_to_quantizer import \
-    get_activation_inferable_quantizer_kwargs
 from model_compression_toolkit.gptq.pytorch.quantizer.base_pytorch_gptq_quantizer import \
     BasePytorchGPTQTrainableQuantizer
 from mct_quantizers import QuantizationTarget
-from mct_quantizers.common.get_quantizers import get_inferable_quantizer_class
 from mct_quantizers.pytorch.quantizers import BasePyTorchInferableQuantizer
 
 from model_compression_toolkit.logger import Logger

model_compression_toolkit/qat/pytorch/quantization_facade.py

@@ -110,7 +110,6 @@ def pytorch_quantization_aware_training_init_experimental(in_model: Module,
              User information that may be needed to handle the quantized model.
 
          Examples:
-
              Import MCT:
 
              >>> import model_compression_toolkit as mct
@@ -120,21 +119,19 @@ def pytorch_quantization_aware_training_init_experimental(in_model: Module,
              >>> from torchvision.models import mobilenet_v2
              >>> model = mobilenet_v2(pretrained=True)
 
-            Create a random dataset generator, for required number of calibration iterations (num_calibration_batches):
-            In this example a random dataset of 10 batches each containing 4 images is used.
+             Create a random dataset generator, for required number of calibration iterations (num_calibration_batches). In this example, a random dataset of 10 batches each containing 4 images is used:
 
-            >>> import numpy as np
-            >>> num_calibration_batches = 10
-            >>> def repr_datagen():
-            >>>     for _ in range(num_calibration_batches):
-            >>>         yield [np.random.random((4, 3, 224, 224))]
+             >>> import numpy as np
+             >>> num_calibration_batches = 10
+             >>> def repr_datagen():
+             >>>     for _ in range(num_calibration_batches):
+             >>>         yield [np.random.random((4, 3, 224, 224))]
 
              Create a MCT core config, containing the quantization configuration:
 
              >>> config = mct.core.CoreConfig()
 
-             Pass the model, the representative dataset generator, the configuration and the target resource utilization to get a
-             quantized model. Now the model contains quantizer wrappers for fine tunning the weights:
+             Pass the model, the representative dataset generator, the configuration and the target resource utilization to get a quantized model. Now the model contains quantizer wrappers for fine tunning the weights:
 
              >>> quantized_model, quantization_info = mct.qat.pytorch_quantization_aware_training_init_experimental(model, repr_datagen, core_config=config)
 
@@ -149,8 +146,8 @@ def pytorch_quantization_aware_training_init_experimental(in_model: Module,
         if core_config.is_mixed_precision_enabled:
             if not isinstance(core_config.mixed_precision_config, MixedPrecisionQuantizationConfig):
                 Logger.critical("Given quantization config to mixed-precision facade is not of type "
-                             "MixedPrecisionQuantizationConfig. Please use pytorch_post_training_quantization API,"
-                             "or pass a valid mixed precision configuration.")
+                                "MixedPrecisionQuantizationConfig. Please use pytorch_post_training_quantization API,"
+                                "or pass a valid mixed precision configuration.")
 
         tb_w = init_tensorboard_writer(DEFAULT_PYTORCH_INFO)
         fw_impl = PytorchImplementation()