Merge branch 'pulp-platform:devel' into gap9-pr

Author: runwangdl

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

@@ -73,6 +73,10 @@ jobs:
         testFloatSoftmax
         testFloatTranspose
         testFloatMul
+        testFloatPowScalar
+        testFloatPowVector
+        testFloatSqrt
+        testFloatRMSNorm
         Quant
         Dequant
         QuantizedLinear

.gitignore

@@ -24,6 +24,8 @@ package-lock.json
 .mypy_cache
 node_modules
 
+.venv/*
+
 compile_commands.json
 
 docs/_autosummary

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 RMSNorm (Pow and Sqrt operators) [#136](https://github.com/pulp-platform/Deeploy/pull/136)
 - Demo TinyViT compatibility with tiled Siracusa [#124](https://github.com/pulp-platform/Deeploy/pull/124)
 - TinyViT on non-tiled Siracusa [#117](https://github.com/pulp-platform/Deeploy/pull/117)
 - Support Fully Asynchronous DMAs [#114](https://github.com/pulp-platform/Deeploy/pull/114)
@@ -26,6 +27,8 @@ This file contains the changelog for the Deeploy project. The changelog is divid
 - Fix bias hoisting in generic GEMM with no bias [#126](https://github.com/pulp-platform/Deeploy/pull/126)
 
 ### Added
+- Support for RMSNorm operation via operator decomposition.
+- Added `Pow` (Power) and `Sqrt` (Square Root) operation support (Parsers, Layers, Bindings, Templates, and FP32 Kernels) for the Generic platform.
 - Support for input tiling for PULP FP regular and DW conv 2D.
 - CI tests for tiled Siracusa FP regular and DW conv 2D, with and without bias, for skip connections, and for the demo version of TinyViT.
 - Documentation for PULP FP regular and DW conv 2D and MatMul tile constraints.

Deeploy/Targets/Generic/Bindings.py

@@ -15,11 +15,11 @@
     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
+    FloatPowTemplate, FloatReduceMeanTemplate, FloatReluTemplate, FloatSoftmaxTemplate, FloatSqrtTemplate, \
+    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, \
@@ -118,6 +118,16 @@
         BasicTransformer)
 ]
 
+BasicPowBindings = [
+    NodeBinding(DummyChecker([PointerClass(float32_t), PointerClass(float32_t)], [PointerClass(float32_t)]),
+                FloatPowTemplate.referenceTemplate, BasicTransformer),
+]
+
+BasicSqrtBindings = [
+    NodeBinding(DummyChecker([PointerClass(float32_t)], [PointerClass(float32_t)]), FloatSqrtTemplate.referenceTemplate,
+                BasicTransformer),
+]
+
 BasicDivBindings = [
     NodeBinding(DivChecker([PointerClass(int32_t), PointerClass(int32_t)], [PointerClass(int32_t)]),
                 IntegerDivTemplate.referenceTemplate, BasicTransformer)

Deeploy/Targets/Generic/Layers.py

@@ -227,6 +227,18 @@ def computeOps(self):
         return matmul + rqs
 
 
+class PowLayer(ONNXLayer):
+
+    def __init__(self, maps: List[NodeMapper]):
+        super().__init__(maps)
+
+
+class SqrtLayer(ONNXLayer):
+
+    def __init__(self, maps: List[NodeMapper]):
+        super().__init__(maps)
+
+
 class DivLayer(ONNXLayer):
 
     def __init__(self, maps: List[NodeMapper]):

Deeploy/Targets/Generic/Parsers.py

@@ -8,7 +8,7 @@
 import numpy as np
 import onnx_graphsurgeon as gs
 
-from Deeploy.DeeployTypes import NetworkContext, NodeParser, VariableBuffer
+from Deeploy.DeeployTypes import ConstantBuffer, NetworkContext, NodeParser, VariableBuffer
 
 
 class ConcatParser(NodeParser):
@@ -1964,6 +1964,32 @@ def parseNodeCtxt(self,
         return ctxt, True
 
 
+class PowParser(NodeParser):
+
+    def __init__(self):
+        super().__init__()
+
+    def parseNode(self, node: gs.Node) -> bool:
+        return node.op == 'Pow' and len(node.inputs) == 2 and len(node.outputs) == 1
+
+    def parseNodeCtxt(self,
+                      ctxt: NetworkContext,
+                      node: gs.Node,
+                      channels_first: bool = True) -> Tuple[NetworkContext, bool]:
+
+        # Lookup both inputs (data and exponent)
+        data_in = ctxt.lookup(node.inputs[0].name)
+        exponent_tensor = ctxt.lookup(node.inputs[1].name)
+        data_out = ctxt.lookup(node.outputs[0].name)
+
+        self.operatorRepresentation['data_in'] = data_in.name
+        self.operatorRepresentation['exponent'] = exponent_tensor.name
+        self.operatorRepresentation['data_out'] = data_out.name
+        self.operatorRepresentation['size'] = int(np.prod(data_in.shape))
+
+        return ctxt, True
+
+
 class DivParser(NodeParser):
 
     def __init__(self):
@@ -2747,3 +2773,26 @@ def parseNodeCtxt(self,
                 "ch_im_out"] * self.operatorRepresentation["dim_im_out_y"]
             return newCtxt, True
         return ctxt, False
+
+
+class SqrtParser(NodeParser):
+
+    def __init__(self):
+        super().__init__()
+
+    def parseNode(self, node: gs.Node) -> bool:
+        return node.op == 'Sqrt' and len(node.inputs) == 1 and len(node.outputs) == 1
+
+    def parseNodeCtxt(self,
+                      ctxt: NetworkContext,
+                      node: gs.Node,
+                      channels_first: bool = True) -> Tuple[NetworkContext, bool]:
+
+        data_in = ctxt.lookup(node.inputs[0].name)
+        data_out = ctxt.lookup(node.outputs[0].name)
+
+        self.operatorRepresentation['data_in'] = data_in.name
+        self.operatorRepresentation['data_out'] = data_out.name
+        self.operatorRepresentation['size'] = int(np.prod(data_in.shape))
+
+        return ctxt, True

Deeploy/Targets/Generic/Platform.py

@@ -11,21 +11,22 @@
     BasicDequantBindings, BasicDivBindings, BasicDWConv1DBinding, BasicDWConv2DBindings, BasicGatherBindings, \
     BasicGELUBindings, BasicGEMMBindings, BasicITAPartialSoftmaxBinding, BasicITASoftmaxBinding, \
     BasicLayerNormBindings, BasicMatMulBindings, BasicMaxPool1DBindings, BasicMaxPool2DBindings, BasicMulBindings, \
-    BasicPad1DBindings, BasicPad2DBindings, BasicQuantBindings, BasicReduceMeanBindings, BasicReduceSumBindings, \
-    BasicReluBinding, BasicReshapeBindings, BasicRQIntegerDivBinding, BasicRQSBindings, BasicRQSGELUBinding, \
-    BasicSliceBindings, BasicSoftmaxBindings, BasicTransposeBindings, DummyBinding
+    BasicPad1DBindings, BasicPad2DBindings, BasicPowBindings, BasicQuantBindings, BasicReduceMeanBindings, \
+    BasicReduceSumBindings, BasicReluBinding, BasicReshapeBindings, BasicRQIntegerDivBinding, BasicRQSBindings, \
+    BasicRQSGELUBinding, BasicSliceBindings, BasicSoftmaxBindings, BasicSqrtBindings, BasicTransposeBindings, \
+    DummyBinding
 from Deeploy.Targets.Generic.Layers import AddLayer, BatchNormalizationLayer, ConcatLayer, ConvLayer, \
     ConvTransposeLayer, DebugPrintLayer, DequantLayer, DivLayer, GatherLayer, GELULayer, GEMMLayer, ITAMaxLayer, \
-    LayerNormLayer, MatMulLayer, MaxPoolLayer, MulLayer, PadLayer, QuantLayer, ReduceMeanLayer, ReduceSumLayer, \
-    ReluLayer, RequantShiftLayer, ReshapeLayer, RQIntegerDivLayer, RQSiGELULayer, SliceLayer, SoftmaxLayer, \
-    TransposeLayer
+    LayerNormLayer, MatMulLayer, MaxPoolLayer, MulLayer, PadLayer, PowLayer, QuantLayer, ReduceMeanLayer, \
+    ReduceSumLayer, ReluLayer, RequantShiftLayer, ReshapeLayer, RQIntegerDivLayer, RQSiGELULayer, SliceLayer, \
+    SoftmaxLayer, SqrtLayer, TransposeLayer
 from Deeploy.Targets.Generic.Parsers import AddParser, BatchNormParser, ConcatParser, ConvTranspose1DParser, \
     DebugParser, DequantParser, DivParser, DummyParser, FlattenParser, GatherParser, GELUParser, GenericConv1DParser, \
     GenericConv2DParser, GenericDWConv1DParser, GenericDWConv2DParser, GenericGEMMParser, GenericMaxPool2DParser, \
     IntegerDivParser, ITAMaxParser, ITAPartialMaxParser, LayerNormParser, MatMulParser, MaxPool1DParser, MulParser, \
-    Pad1DParser, Pad2DParser, QuantParser, ReduceMeanParser, ReduceSumParser, ReluParser, RequantShiftParser, \
-    ReshapeParser, RQIntegerDivParser, RQSiGELUParser, SliceParser, SoftmaxParser, TransposeParser, UnsqueezeParser, \
-    iLayerNormParser, iSoftmaxParser
+    Pad1DParser, Pad2DParser, PowParser, QuantParser, ReduceMeanParser, ReduceSumParser, ReluParser, \
+    RequantShiftParser, ReshapeParser, RQIntegerDivParser, RQSiGELUParser, SliceParser, SoftmaxParser, SqrtParser, \
+    TransposeParser, UnsqueezeParser, iLayerNormParser, iSoftmaxParser
 from Deeploy.Targets.Generic.Templates import AllocateTemplate, FreeTemplate
 from Deeploy.Targets.Generic.TopologyOptimizationPasses.Passes import DequantPatternPass, ExtractPaddingFromConvPass, \
     ExtractPaddingFromPoolPass, MatMulAddMergePass, MergeConstAddAndRequantPass, QuantPatternPass, \
@@ -52,6 +53,8 @@
 MaxPoolMapper = NodeMapper(GenericMaxPool2DParser(), BasicMaxPool2DBindings)
 MaxPool1DMapper = NodeMapper(MaxPool1DParser(), BasicMaxPool1DBindings)
 MulMapper = NodeMapper(MulParser(), BasicMulBindings)
+PowMapper = NodeMapper(PowParser(), BasicPowBindings)
+SqrtMapper = NodeMapper(SqrtParser(), BasicSqrtBindings)
 Pad1DMapper = NodeMapper(Pad1DParser(), BasicPad1DBindings)
 Pad2DMapper = NodeMapper(Pad2DParser(), BasicPad2DBindings)
 ReduceMeanMapper = NodeMapper(ReduceMeanParser(), BasicReduceMeanBindings)
@@ -98,6 +101,8 @@
     'MatMulInteger': MatMulLayer([MatMulMapper]),
     'MaxPool': MaxPoolLayer([MaxPool1DMapper, MaxPoolMapper]),
     'Mul': MulLayer([MulMapper]),
+    'Pow': PowLayer([PowMapper]),
+    'Sqrt': SqrtLayer([SqrtMapper]),
     'Pad': PadLayer([Pad1DMapper, Pad2DMapper]),
     'ReduceMean': ReduceMeanLayer([ReduceMeanMapper]),
     'ReduceSum': ReduceSumLayer([ReduceSumMapper]),

Deeploy/Targets/Generic/Templates/FloatPowTemplate.py

@@ -0,0 +1,59 @@
+# SPDX-FileCopyrightText: 2025 ETH Zurich and University of Bologna
+#
+# SPDX-License-Identifier: Apache-2.0
+from typing import Dict, List, Tuple
+
+import numpy as np
+
+from Deeploy.DeeployTypes import NetworkContext, NodeTemplate, OperatorRepresentation
+
+
+class _PowTemplate(NodeTemplate):
+
+    def alignToContext(self, ctxt: NetworkContext,
+                       operatorRepresentation: OperatorRepresentation) -> Tuple[NetworkContext, Dict, List[str]]:
+        # Get input and output tensors
+        data_in = ctxt.lookup(operatorRepresentation['data_in'])
+        exponent = ctxt.lookup(operatorRepresentation['exponent'])
+        data_out = ctxt.lookup(operatorRepresentation['data_out'])
+
+        # Get data type (fp32)
+        data_type = data_in._type.typeName
+        operatorRepresentation['data_type'] = data_type
+
+        # Get type width dynamically (e.g., 32, 64)
+        type_width = data_in._type.referencedType.typeWidth
+        operatorRepresentation['type_width'] = type_width
+
+        # Calculate size
+        input_size = int(np.prod(data_in.shape))
+        exponent_size = int(np.prod(exponent.shape))
+        operatorRepresentation['size'] = input_size
+
+        # Check if exponent is scalar (broadcasting)
+        if exponent_size == 1:
+            operatorRepresentation['is_scalar'] = True
+            # Get the full variable name with prefix
+            exponent_name = operatorRepresentation['exponent']
+            operatorRepresentation['exponent_scalar'] = f"DeeployNetwork_{exponent_name}[0]"
+        else:
+            # Since currently the kernel only supports equally sized base-exponent data,
+            # for non-scalar, let's add a size check here (length of data_in should be equal to exponent length).
+            if input_size != exponent_size:
+                raise ValueError(f"Pow operator mismatch: input size ({input_size}) "
+                                 f"must equal exponent size ({exponent_size}) for non-scalar exponents.")
+
+            operatorRepresentation['is_scalar'] = False
+            operatorRepresentation['exponent_scalar'] = "NULL"
+
+        return ctxt, operatorRepresentation, []
+
+
+referenceTemplate = _PowTemplate("""
+// Pow (Name: ${nodeName}, Op: ${nodeOp})
+% if is_scalar:
+Pow_fp${type_width}_scalar_fp${type_width}(${data_in}, ${exponent_scalar}, ${data_out}, ${size});
+% else:
+Pow_fp${type_width}_fp${type_width}_fp${type_width}(${data_in}, ${exponent}, ${data_out}, ${size});
+% endif
+""")

Deeploy/Targets/Generic/Templates/FloatSqrtTemplate.py

@@ -0,0 +1,35 @@
+# SPDX-FileCopyrightText: 2025 ETH Zurich and University of Bologna
+#
+# SPDX-License-Identifier: Apache-2.0
+from typing import Dict, List, Tuple
+
+import numpy as np
+
+from Deeploy.DeeployTypes import NetworkContext, NodeTemplate, OperatorRepresentation
+
+
+class _SqrtTemplate(NodeTemplate):
+
+    def alignToContext(self, ctxt: NetworkContext,
+                       operatorRepresentation: OperatorRepresentation) -> Tuple[NetworkContext, Dict, List[str]]:
+        # Get input and output tensors
+        data_in = ctxt.lookup(operatorRepresentation['data_in'])
+        data_out = ctxt.lookup(operatorRepresentation['data_out'])
+
+        # Get data type (fp32)
+        data_type = data_in._type.typeName
+        operatorRepresentation['data_type'] = data_type
+
+        type_width = data_in._type.referencedType.typeWidth
+        operatorRepresentation['type_width'] = type_width
+
+        # Calculate size
+        operatorRepresentation['size'] = int(np.prod(data_in.shape))
+
+        return ctxt, operatorRepresentation, []
+
+
+referenceTemplate = _SqrtTemplate("""
+// Sqrt (Name: ${nodeName}, Op: ${nodeOp})
+Sqrt_fp${type_width}_fp${type_width}(${data_in}, ${data_out}, ${size});
+""")