Support for 1D Autoencoder (#98)

This PR adds required kernels, binding, and templates to support 1D autoencoders.

## Added
- Added support for 1D Autoencoder operations, including newly implemented BatchNorm, 1D ConvTranspose, and 1D MaxPool layers.
- Added `Autoencoder1D` test to the CI.

Author: Aldrago98

.github/workflows/ci-platform-generic.yml

@@ -96,3 +96,4 @@ jobs:
         CCT/CCT_1_16_16_8
         CCT/CCT_2_32_32_128_Opset20
         testFloatDemoTinyViT
+        Autoencoder1D

CHANGELOG.md

@@ -4,6 +4,7 @@ This file contains the changelog for the Deeploy project. The changelog is divid
 ## Unreleased (Planned Release Target: v0.2.1)
 
 ### List of Pull Requests
+- Support for 1D Autoencoder [#98](https://github.com/pulp-platform/Deeploy/pull/98)
 - Refactor Logging for Improved Debugging [#115](https://github.com/pulp-platform/Deeploy/pull/115)
 - Add reuse-tool as an SPDX license header linter [#113](https://github.com/pulp-platform/Deeploy/pull/113)
 - Bug fixes, API Cleanup and Reduce Compiler Warning on PULP [#112](https://github.com/pulp-platform/Deeploy/pull/112)
@@ -158,6 +159,13 @@ This release containing major architectural changes, new platform support, enhan
 
 
 ### Added
+- BatchNorm kernel 
+- ConvTranspose kernel 
+- MaxPool1D kernel 
+- Template for 1D Convolution
+- Support for float32 data type in the previous kernels
+- Float binding for Pad1D kernel
+- Test for Autoencoder1D in the CI pipeline
 - ChimeraDeployer, currently mainly a placeholder
 - Allocate templates for Chimera
 - ChimeraPlatform, using appropriate allocation templates and using the generic Parser + Binding for the Add node
@@ -291,6 +299,8 @@ This release containing major architectural changes, new platform support, enhan
 - `dev-requirements.txt` tracking the dependencies of the build system, linting, documentation, and QOL.
 
 ### Changed
+- FloatConvTemplate file
+- Platform.py file  
 - Bump the CMake version to 3.24 as required for the chimera-sdk
 - Bump GVSoC's version and add chimera simulation target
 - Rename the generic source util to utils to avoid name collision with chimera-sdk

Deeploy/Targets/Generic/Bindings.py

@@ -11,19 +11,20 @@
     int8_t, int32_t, uint8_t
 from Deeploy.DeeployTypes import CodeTransformation, NodeBinding
 from Deeploy.FutureExtension.CodeTransformationPasses.FutureCodeTransformation import FutureGeneration
-from Deeploy.Targets.Generic.Templates import AddTemplate, ConcatTemplate, ConvTemplate, DebugPrintTemplate, \
-    DequantTemplate, DummyTemplate, DWConvTemplate, FloatAddTemplate, FloatConvTemplate, FloatDivTemplate, \
-    FloatDWConvTemplate, FloatGELUTemplate, FloatGemmTemplate, FloatLayernormTemplate, FloatMatMulTemplate, \
-    FloatMaxPoolTemplate, FloatMulTemplate, FloatPadTemplate, FloatReduceMeanTemplate, FloatReluTemplate, \
-    FloatSoftmaxTemplate, GatherTemplate, GemmTemplate, IntegerDivTemplate, ITAMaxTemplate, ITAPartialMaxTemplate, \
-    MatMulTemplate, MaxPoolTemplate, MulTemplate, PadTemplate, QuantTemplate, ReduceMeanTemplate, ReduceSumTemplate, \
-    RequantShiftTemplate, ReshapeTemplate, RQIntegerDivTemplate, RQSiGELUTemplate, SliceTemplate, TransposeTemplate, \
-    iGELUTemplate, iLayernormTemplate, iRMSNormTemplate, iSoftmaxTemplate
-from Deeploy.Targets.Generic.TypeCheckers import AddChecker, ConcatChecker, ConvChecker, DebugPrintChecker, \
-    DequantChecker, DivChecker, DummyChecker, GatherChecker, GELUChecker, GEMMChecker, LayerNormChecker, \
-    MatMulChecker, MaxPoolChecker, MulChecker, PadChecker, QuantChecker, ReduceMeanChecker, ReduceSumChecker, \
-    ReluChecker, RequantShiftChecker, ReshapeChecker, RQIntegerDivChecker, SliceChecker, SoftmaxChecker, \
-    TransposeChecker
+from Deeploy.Targets.Generic.Templates import AddTemplate, BatchNormalizationTemplate, ConcatTemplate, ConvTemplate, \
+    ConvTransposeTemplate, DebugPrintTemplate, DequantTemplate, DummyTemplate, DWConvTemplate, FloatAddTemplate, \
+    FloatConvTemplate, FloatDivTemplate, FloatDWConvTemplate, FloatGELUTemplate, FloatGemmTemplate, \
+    FloatLayernormTemplate, FloatMatMulTemplate, FloatMaxPoolTemplate, FloatMulTemplate, FloatPadTemplate, \
+    FloatReduceMeanTemplate, FloatReluTemplate, FloatSoftmaxTemplate, GatherTemplate, GemmTemplate, \
+    IntegerDivTemplate, ITAMaxTemplate, ITAPartialMaxTemplate, MatMulTemplate, MaxPoolTemplate, MulTemplate, \
+    PadTemplate, QuantTemplate, ReduceMeanTemplate, ReduceSumTemplate, RequantShiftTemplate, ReshapeTemplate, \
+    RQIntegerDivTemplate, RQSiGELUTemplate, SliceTemplate, TransposeTemplate, iGELUTemplate, iLayernormTemplate, \
+    iRMSNormTemplate, iSoftmaxTemplate
+from Deeploy.Targets.Generic.TypeCheckers import AddChecker, BatchNormChecker, ConcatChecker, ConvChecker, \
+    DebugPrintChecker, DequantChecker, DivChecker, DummyChecker, GatherChecker, GELUChecker, GEMMChecker, \
+    LayerNormChecker, MatMulChecker, MaxPoolChecker, MulChecker, PadChecker, QuantChecker, ReduceMeanChecker, \
+    ReduceSumChecker, ReluChecker, RequantShiftChecker, ReshapeChecker, RQIntegerDivChecker, SliceChecker, \
+    SoftmaxChecker, TransposeChecker
 
 BasicTransformer = CodeTransformation([ArgumentStructGeneration(), MemoryManagementGeneration(), FutureGeneration()])
 
@@ -53,8 +54,14 @@
                 FloatAddTemplate.referenceTemplate, BasicTransformer)
 ]
 
-BasicConv1DBinding = NodeBinding(ConvChecker([PointerClass(int8_t), PointerClass(int8_t)], [PointerClass(int32_t)]),
-                                 ConvTemplate.reference1DTemplate, BasicTransformer)
+BasicConv1DBindings = [
+    NodeBinding(ConvChecker(
+        [PointerClass(type), PointerClass(type), PointerClass(type)], [PointerClass(type)]),
+                FloatConvTemplate.reference1DTemplate, BasicTransformer) for type in FloatDataTypes
+] + [
+    NodeBinding(ConvChecker([PointerClass(int8_t), PointerClass(int8_t)], [PointerClass(int32_t)]),
+                ConvTemplate.reference1DTemplate, BasicTransformer)
+]
 
 BasicDWConv1DBinding = NodeBinding(ConvChecker([PointerClass(int8_t), PointerClass(int8_t)], [PointerClass(int32_t)]),
                                    DWConvTemplate.reference1DTemplate, BasicTransformer)
@@ -147,6 +154,11 @@
                 FloatMatMulTemplate.referenceTemplate, BasicTransformer)
 ]
 
+BasicMaxPool1DBindings = [
+    NodeBinding(MaxPoolChecker([PointerClass(type)], [PointerClass(type)]), FloatMaxPoolTemplate.reference1DTemplate,
+                BasicTransformer) for type in FloatDataTypes
+]
+
 BasicMaxPool2DBindings = [
     NodeBinding(MaxPoolChecker([PointerClass(int8_t)], [PointerClass(int8_t)]), MaxPoolTemplate.referenceTemplate,
                 BasicTransformer)
@@ -167,7 +179,11 @@
 BasicPad1DBindings = [
     NodeBinding(PadChecker([PointerClass(type)], [PointerClass(type)]), PadTemplate.reference1DTemplate,
                 BasicTransformer) for type in SignedIntegerDataTypes
+] + [
+    NodeBinding(PadChecker([PointerClass(type)], [PointerClass(type)]), FloatPadTemplate.reference1DTemplate,
+                BasicTransformer) for type in FloatDataTypes
 ]
+
 BasicPad2DBindings = [
     NodeBinding(PadChecker([PointerClass(type)], [PointerClass(type)]), PadTemplate.reference2DTemplate,
                 BasicTransformer) for type in SignedIntegerDataTypes
@@ -266,3 +282,30 @@
     NodeBinding(DequantChecker([PointerClass(int32_t)], [PointerClass(float32_t)]), DequantTemplate.referenceTemplate,
                 BasicTransformer),
 ]
+
+BasicBatchNormBindings = [
+    NodeBinding(
+        BatchNormChecker(
+            [PointerClass(type),
+             PointerClass(type),
+             PointerClass(type),
+             PointerClass(type),
+             PointerClass(type)], [PointerClass(type)]), BatchNormalizationTemplate.referenceTemplate, BasicTransformer)
+    for type in FloatDataTypes
+]
+
+BasicConvTransposeBindings = [
+    NodeBinding(
+        ConvChecker(
+            [PointerClass(type), PointerClass(type), PointerClass(type)],  # input, weight, bias
+            [PointerClass(type)]),
+        ConvTransposeTemplate.referenceTemplate,
+        BasicTransformer) for type in FloatDataTypes
+] + [
+    NodeBinding(
+        ConvChecker(
+            [PointerClass(type), PointerClass(type)],  # input, weight
+            [PointerClass(type)]),
+        ConvTransposeTemplate.referenceTemplate,
+        BasicTransformer) for type in FloatDataTypes
+]

Deeploy/Targets/Generic/Layers.py

@@ -618,3 +618,64 @@ class DequantLayer(ONNXLayer):
 
     def __init__(self, maps: List[NodeMapper]):
         super().__init__(maps)
+
+
+class BatchNormalizationLayer(ONNXLayer):
+
+    def __init__(self, maps: List[NodeMapper]):
+        super().__init__(maps)
+
+    def computeOps(self):
+        # 5 operations per element: sub, mul, add, sqrt, div
+        B = self.mapper.parser.operatorRepresentation['batch_size']
+        C = self.mapper.parser.operatorRepresentation['channel_size']
+        W = self.mapper.parser.operatorRepresentation['window_size']
+        return B * C * W * 5
+
+
+class ConvTransposeLayer(ONNXLayer):
+
+    def __init__(self, maps: List[NodeMapper]):
+        super().__init__(maps)
+
+    def computeShapes(self, inputShapes: Shape, outputShapes: Shape, operatorRepresentation,
+                      channels_first) -> Tuple[Shape, Shape]:
+        """
+        Infers output shapes for ConvTranspose using only static info.
+        - inputShapes[0]: input tensor shape (e.g., [N, C_in, W] for 1D, [N, C_in, H, W] for 2D)
+        - inputShapes[1]: weight tensor shape (e.g., [C_in, C_out // group, kW] for 1D)
+        - outputShapes[0]: output tensor shape (to be updated)
+        """
+        newInputShapes = list(inputShapes)
+        newOutputShapes = list(outputShapes)
+        group = operatorRepresentation.get('group', 1)
+        weight_shape = inputShapes[1]
+
+        if newOutputShapes and len(newOutputShapes[0]) >= 2:
+            # For 1D: weight_shape = [C_in, C_out // group, kW]
+            # For 2D: weight_shape = [C_in, C_out // group, kH, kW]
+            ch_out = weight_shape[1] * group
+            if channels_first:
+                newOutputShapes[0][1] = ch_out
+            else:
+                newOutputShapes[0][-1] = ch_out
+
+        return newInputShapes, newOutputShapes
+
+    def computeOps(self):
+        opRep = self.mapper.parser.operatorRepresentation
+
+        groups = opRep.get('group', 1)
+        kernel_shape = np.prod(opRep['kernel_shape'])  # es. [3, 3] -> 9
+        ch_in = opRep['ch_im_in']
+        ch_out = opRep['ch_im_out']
+
+        opsPerPx = int(kernel_shape * ch_in * ch_out / groups) * 2
+
+        # ConvTranspose upscales spatial dims, quindi num pixel viene da output
+        if 'dim_im_out_y' in opRep:
+            numPx = opRep['dim_im_out_x'] * opRep['dim_im_out_y']
+        else:
+            numPx = opRep['dim_im_out_x']
+
+        return numPx * opsPerPx