[DRAFT] Demo TinyViT compatibility with tiled Siracusa

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

@@ -53,7 +53,15 @@ jobs:
         testBacktracking
         testFloatAdder
         testFloatGEMM
+
         testFloat2DConvolution
+        testFloat2DConvolutionBias
+        testFloat2DConvolutionZeroBias
+
+        testFloat2DDWConvolution
+        testFloat2DDWConvolutionBias
+        testFloat2DDWConvolutionZeroBias
+
         testFloatLayerNorm
         testFloatRelu
         testFloatMaxPool
@@ -64,6 +72,7 @@ jobs:
         Quant
         Dequant
         testFloatReduceSum
+        testFloatReshapeWithSkipConnection
         testFloatSoftmaxGrad
         testFloatSoftmaxCrossEntropy
         testFloatSoftmaxCrossEntropyGrad
@@ -87,4 +96,5 @@ jobs:
         CCT/CCT_1_16_16_8
         CCT/CCT_2_32_32_128_Opset20
         testTrainCCT/CCT1_Classifier_Training/CCT_1_16_16_8
+        testFloatDemoTinyViT
       num-cores: 8

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
+- 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)
 - Disallow shape inference [#128](https://github.com/pulp-platform/Deeploy/pull/128)
 - Remove memory-aware node bindings [#123](https://github.com/pulp-platform/Deeploy/pull/123)
@@ -24,6 +25,13 @@ 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
+- PULP 2D FP DW conv Im2Col template and kernel, with bias support.
+- Bias support for PULP 2D FP regular conv Im2Col in template & kernel.
+- PULP FP DW conv 2D parser.
+- FP conv 2D (simple & DW), reshape & skip connection, and TinyViT demo tests to the non-tiled Siracusa CI pipeline.
+- FP bindings and mappings for PULP slice, DW conv 2D, and reduce mean operations.
+- FP PULP DW conv lowering optimization pass similar to the existent one for integer version.
+- RemoveEmptyConvBiasPass to the PULP optimizer.
 - Add manual type inference feature (CLI: `--input-type-map`/`--input-offset-map`) to resolve ambiguities when test inputs are not representative enough
 - Added a `testTypeInferenceDifferentTypes` test case to validate type inference for different input types
 - Added `_mangleNodeNames` function to avoid duplicate node mappings
@@ -60,6 +68,7 @@ This file contains the changelog for the Deeploy project. The changelog is divid
 - Added new waiting-strategy logic with fine-grained `PerTensorWaitingStrategy`
 
 ### Changed
+- Reduced size of reshape & skip connection test, for non-tiled Siracusa memory compatibility.
 - Replaced platform-specific tags (`*-amd64`, `*-arm64`) with direct digest references in `Noelware/docker-manifest-action`.
 - mchan HAL is now reduced to bare-bones
 - refactor of the IntrospectiveCodeTransformation to work on the Mako template
@@ -97,6 +106,9 @@ This file contains the changelog for the Deeploy project. The changelog is divid
 - Refactored DMA code generation (`SnitchDma`, `Mchan`) to correctly overlap transfers and compute in double-buffering mode
 
 ### Fixed
+- Fixed bug for non-batched elements in the PULPOpen FP GEMM and matmul templates.
+- Added underscore to the beginning of closure names to avoid naming issues when they start with unsupported first characters (like numbers).
+- Data types in the PULPOpen FP add and mul templates.
 - Prevent node duplication for graphs generated via GraphSurgeon
 - Resolved issue with missing `id` in the `Build Cache for Docker` step, used in the `Inject build-cache` step.
 - Fix license CI check and prevent potential issues with `jq` installation
@@ -185,9 +197,9 @@ This release containing major architectural changes, new platform support, enhan
 
 
 ### Added
-- BatchNorm kernel 
-- ConvTranspose kernel 
-- MaxPool1D kernel 
+- BatchNorm kernel
+- ConvTranspose kernel
+- MaxPool1D kernel
 - Template for 1D Convolution
 - Support for float32 data type in the previous kernels
 - Float binding for Pad1D kernel
@@ -326,7 +338,7 @@ This release containing major architectural changes, new platform support, enhan
 
 ### Changed
 - FloatConvTemplate file
-- Platform.py 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/CommonExtensions/CodeTransformationPasses/Closure.py

@@ -155,7 +155,8 @@ def apply(self,
               executionBlock: ExecutionBlock,
               name: str,
               verbose: CodeGenVerbosity = _NoVerbosity) -> Tuple[NetworkContext, ExecutionBlock]:
-        self.closureName = name + self.closureSuffix
+        # Prepend underscore to avoid name issues when beginning with problematic characters (like numbers)
+        self.closureName = "_" + name + self.closureSuffix
         self.functionCall = executionBlock.generate(ctxt)
         self._generateClosureStruct(ctxt, executionBlock)
         ctxt = self._generateClosureCtxt(ctxt, name)

Deeploy/CommonExtensions/DataTypes.py

@@ -87,11 +87,11 @@ class float64_t(FloatImmediate):
 
 SignedIntegerDataTypes: Tuple[Type[IntegerImmediate], ...] = (int8_t, int16_t, int32_t, int64_t)
 UnsignedIntegerDataTypes: Tuple[Type[IntegerImmediate], ...] = (uint8_t, uint16_t, uint32_t, uint64_t)
-IntegerDataTypes: Tuple[Type[IntegerImmediate], ...] = (sorted((
-    *SignedIntegerDataTypes,
-    *UnsignedIntegerDataTypes,
-),
-                                                               key = lambda _type: _type.typeWidth))
+IntegerDataTypes: Tuple[Type[IntegerImmediate], ...] = tuple(
+    sorted((
+        *SignedIntegerDataTypes,
+        *UnsignedIntegerDataTypes,
+    ), key = lambda _type: _type.typeWidth))
 FloatDataTypes: Tuple[Type[FloatImmediate], ...] = (bfloat16_t, float16_t, float32_t, float64_t)
 
 

Deeploy/CommonExtensions/NetworkDeployers/NetworkDeployerWrapper.py

@@ -2,11 +2,9 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import Any, Union
-
 import onnx_graphsurgeon as gs
 
-from Deeploy.DeeployTypes import CodeGenVerbosity, NetworkContext, NetworkDeployer, ONNXLayer, _NoVerbosity
+from Deeploy.DeeployTypes import CodeGenVerbosity, DeploymentEngine, NetworkContext, NetworkDeployer, _NoVerbosity
 
 
 class NetworkDeployerWrapper(NetworkDeployer):
@@ -68,8 +66,8 @@ def generateBufferAllocationCode(self) -> str:
         return self._innerObject.generateBufferAllocationCode()
 
     # MultiEngineDeployer augment
-    def _mapNode(self, node: gs.Node) -> Union[ONNXLayer, Any]:
-        return self._innerObject._mapNode(node)
+    def _selectEngine(self, node: gs.Node) -> DeploymentEngine:
+        return self._innerObject._selectEngine(node)
 
     def _printMemorySummary(self):
         return self._innerObject._printMemorySummary()

Deeploy/DeeployTypes.py

@@ -325,15 +325,15 @@ def fromNode(cls, node: gs.Node):
         return (cls(name = node.name, shape = node.shape if not isinstance(node, gs.Constant) else node.values.shape))
 
     def has_live_aliases(self, ctxt: NetworkContext) -> bool:
-        """Checks whether this VariableBuffer has any live ancestors, i.e. buffers that are still live and are aliased by this buffer.
+        """Checks whether this VariableBuffer has any live aliases, i.e. buffers that are still live and are aliased by this buffer.
         Parameters
         ----------
         ctxt : NetworkContext
             Current NetworkContext
         Returns
         -------
         bool
-            True if this VariableBuffer has any live ancestors, False otherwise
+            True if this VariableBuffer has any live aliases, False otherwise
         """
         # Do a breadth-first search across the aliasing double-linked list
         live = self._live
@@ -2562,10 +2562,10 @@ def codeTransform(self, verbose: CodeGenVerbosity = _NoVerbosity):
             self.ctxt = layer.codeTransform(self.ctxt, verbose)
         self.transformed = True
 
-    def _mapNode(self, node: gs.Node) -> Union[ONNXLayer, Any]:
+    def _selectEngine(self, node: gs.Node) -> DeploymentEngine:
         for engine in self.Platform.engines:
             if node.op in engine.Mapping:
-                return engine.Mapping[node.op](node)
+                return engine
         raise RuntimeError(f"No mapping found for node {node.name} with op type {node.op}")
 
     def _bindLayers(self):
@@ -2582,7 +2582,8 @@ def _bindLayers(self):
                 flatSchedule += subGraph
 
         for node in flatSchedule:
-            layer = self._mapNode(node)
+            engine = self._selectEngine(node)
+            layer = engine.Mapping[node.op](node)
             if isinstance(layer, ONNXLayer):
                 log.debug(f"   {SUCCESS_MARK} Bind {node.name} to layer {layer.__class__.__name__}")
                 self.layerBinding[layer.node.name] = layer

Deeploy/EngineExtension/NetworkDeployers/EngineColoringDeployer.py

@@ -2,13 +2,13 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import Any, Callable, Dict, Type, Union
+from typing import Callable, Dict, Type
 
 import onnx_graphsurgeon as gs
 
 from Deeploy.AbstractDataTypes import Pointer
 from Deeploy.CommonExtensions.NetworkDeployers.NetworkDeployerWrapper import NetworkDeployerWrapper
-from Deeploy.DeeployTypes import DeploymentPlatform, NetworkDeployer, ONNXLayer, Schedule, TopologyOptimizer
+from Deeploy.DeeployTypes import DeploymentEngine, DeploymentPlatform, NetworkDeployer, Schedule, TopologyOptimizer
 from Deeploy.EngineExtension.OptimizationPasses.TopologyOptimizationPasses.EngineColoringPasses import \
     EngineColoringPass, EngineMapper
 
@@ -48,14 +48,14 @@ def lower(self, graph: gs.Graph) -> gs.Graph:
         ) == 0, f"Missing engine color for nodes {[node.name for node in uncoloredNodes]} with operations {uncoloredOperations}"
         return graph
 
-    def _mapNode(self, node: gs.Node) -> Union[ONNXLayer, Any]:
+    def _selectEngine(self, node: gs.Node) -> DeploymentEngine:
         assert "engine" in node.attrs, f"Node {node.name} doesn't have an engine color."
         engineName = node.attrs["engine"]
         assert isinstance(engineName, str) and engineName in self.engineDict, \
             f"Node {node.name} has an invalid engine {engineName} assigned."
         engine = self.engineDict[engineName]
         assert node.op in engine.Mapping, f"No mapping found for {node.op} in engine {engine.name}"
-        return engine.Mapping[node.op](node)
+        return engine
 
 
 class EngineColoringDeployerWrapper(EngineColoringDeployer, NetworkDeployerWrapper):

Deeploy/Targets/Generic/Templates/FloatReduceMeanTemplate.py

@@ -8,6 +8,7 @@
 
 
 class _FloatReduceMeanTemplate(NodeTemplate):
+    # WARNING: Currently only supports single axis reducing!
 
     def __init__(self, templateStr):
         super().__init__(templateStr)

Deeploy/Targets/Generic/Templates/ReduceMeanTemplate.py

@@ -8,6 +8,7 @@
 
 
 class _ReduceMeanTemplate(NodeTemplate):
+    # WARNING: Currently only supports single axis reducing!
 
     def __init__(self, templateStr):
         super().__init__(templateStr)

Deeploy/Targets/Generic/Templates/SliceTemplate.py

@@ -10,6 +10,7 @@
 
 
 class _SliceTemplate(NodeTemplate):
+    # WARNING: Currently only supports single axis slicing!
 
     def __init__(self, templateStr):
         super().__init__(templateStr)

Deeploy/Targets/PULPOpen/Bindings.py

@@ -9,13 +9,13 @@
 from Deeploy.CommonExtensions.CodeTransformationPasses.Closure import ClosureGeneration, MemoryAwareClosureGeneration
 from Deeploy.CommonExtensions.CodeTransformationPasses.MemoryAllocation import ArgumentStructGeneration, \
     MemoryManagementGeneration, MemoryPassthroughGeneration
-from Deeploy.CommonExtensions.DataTypes import IntegerDataTypes, SignedIntegerDataTypes, float32_t, int8_t, int32_t, \
-    uint8_t
+from Deeploy.CommonExtensions.DataTypes import FloatDataTypes, IntegerDataTypes, SignedIntegerDataTypes, float32_t, \
+    int8_t, int32_t, int64_t, uint8_t
 from Deeploy.DeeployTypes import CodeTransformation, NodeBinding, NodeTemplate
 from Deeploy.FutureExtension.Bindings.AutoFutureBinding import AutoFutureBinding
 from Deeploy.FutureExtension.CodeTransformationPasses.FutureCodeTransformation import FutureGeneration
-from Deeploy.Targets.Generic.Templates import AddTemplate, ConcatTemplate, DequantTemplate, FloatReduceSumTemplate, \
-    GatherTemplate, QuantTemplate, RQSiGELUTemplate, iHardswishTemplate
+from Deeploy.Targets.Generic.Templates import AddTemplate, ConcatTemplate, DequantTemplate, FloatReduceMeanTemplate, \
+    FloatReduceSumTemplate, GatherTemplate, QuantTemplate, RQSiGELUTemplate, SliceTemplate, iHardswishTemplate
 from Deeploy.Targets.Generic.TypeCheckers import AddChecker, ConcatChecker, ConvChecker, DequantChecker, \
     GatherChecker, GELUChecker, GEMMChecker, HardswishChecker, LayerNormChecker, MatMulChecker, MulChecker, \
     QuantChecker, ReduceMeanChecker, ReluChecker, ReshapeChecker, RQAddChecker, RQHardswishChecker, SGDChecker, \
@@ -27,11 +27,11 @@
 from Deeploy.Targets.PULPOpen.DataTypes import PULPDMAFuture
 from Deeploy.Targets.PULPOpen.DMA.L3Dma import l3DmaHack
 from Deeploy.Targets.PULPOpen.DMA.MchanDma import MchanDma
-from Deeploy.Targets.PULPOpen.Templates import ConvTemplate, FloatAddTemplate, FloatConvTemplate, FloatGELUTemplate, \
-    FloatGemmTemplate, FloatLayernormTemplate, FloatMatMulTemplate, FloatMaxPoolTemplate, FloatMulTemplate, \
-    FloatReluTemplate, FloatSoftmaxTemplate, GEMMTemplate, MatrixVectorTemplate, MaxPool2DTemplate, MulTemplate, \
-    ReduceMeanTemplate, RequantShiftTemplate, ReshapeTemplate, RQAddTemplate, RQSiHardswishTemplate, SGDTemplate, \
-    SliceTemplate, SoftmaxCrossEntropyLossTemplate, TallGEMMTemplate, TransposeTemplate, UniformRequantShiftTemplate, \
+from Deeploy.Targets.PULPOpen.Templates import ConvTemplate, DMASliceTemplate, FloatAddTemplate, FloatConvTemplate, \
+    FloatGELUTemplate, FloatGemmTemplate, FloatLayernormTemplate, FloatMatMulTemplate, FloatMaxPoolTemplate, \
+    FloatMulTemplate, FloatReluTemplate, FloatSoftmaxTemplate, GEMMTemplate, MatrixVectorTemplate, MaxPool2DTemplate, \
+    MulTemplate, ReduceMeanTemplate, RequantShiftTemplate, ReshapeTemplate, RQAddTemplate, RQSiHardswishTemplate, \
+    SGDTemplate, SoftmaxCrossEntropyLossTemplate, TallGEMMTemplate, TransposeTemplate, UniformRequantShiftTemplate, \
     iRMSNormTemplate, iSoftmaxTemplate
 from Deeploy.Targets.PULPOpen.TypeCheckers import PULPConvChecker, PULPLinearChecker, PULPMaxPoolChecker, \
     PULPRequantShiftChecker
@@ -148,16 +148,24 @@
             PointerClass(uint8_t),
             PointerClass(uint8_t),
             PointerClass(uint8_t)
-        ], [PULPDMAFuture(underlyingType = type)]), SliceTemplate.referenceTemplate, MemoryAwareForkTransformer)
+        ], [PULPDMAFuture(underlyingType = type)]), DMASliceTemplate.referenceTemplate, MemoryAwareForkTransformer)
     for type in IntegerDataTypes
 ]
 
+PULPSliceBindings = [
+    NodeBinding(
+        SliceChecker([
+            PointerClass(type),
+            PointerClass(uint8_t),
+            PointerClass(uint8_t),
+            PointerClass(uint8_t),
+            PointerClass(uint8_t)
+        ], [PointerClass(type)]), SliceTemplate.referenceTemplate, ForkTransformer) for type in FloatDataTypes
+]
+
 PULPReshapeBindings = [
-    NodeBinding(ReshapeChecker([PointerClass(type), PointerClass(int32_t)], [PointerClass(type)]),
-                ReshapeTemplate.referenceTemplate, SkipTransformer) for type in IntegerDataTypes
-] + [
-    NodeBinding(ReshapeChecker([PointerClass(float32_t), PointerClass(type)], [PointerClass(float32_t)]),
-                ReshapeTemplate.referenceTemplate, SkipTransformer) for type in IntegerDataTypes
+    NodeBinding(ReshapeChecker([PointerClass(type), PointerClass(int64_t)], [PointerClass(type)]),
+                ReshapeTemplate.referenceTemplate, SkipTransformer) for type in IntegerDataTypes + FloatDataTypes
 ]
 
 PULPRQAddBindings = [
@@ -225,6 +233,14 @@
         ForkTransformer)
 ]
 
+PULPFloatDWConv2DBindings = [
+    NodeBinding(
+        ConvChecker(
+            [PointerClass(float_type), PointerClass(float_type),
+             PointerClass(float_type)], [PointerClass(float_type)]), FloatConvTemplate.referenceDW2DIm2ColTemplate,
+        ForkTransformer) for float_type in FloatDataTypes
+]
+
 PULPRQSMatrixVecBindings = [
     NodeBinding(
         PULPLinearChecker([PointerClass(type1),
@@ -276,6 +292,11 @@
 PULPReduceMeanBindings = [
     NodeBinding(ReduceMeanChecker([PointerClass(type)], [PointerClass(type)]), ReduceMeanTemplate.referenceTemplate,
                 ClusterTransformer) for type in IntegerDataTypes
+] + [
+    NodeBinding(ReduceMeanChecker([PointerClass(float_type), PointerClass(integer_type)], [PointerClass(float_type)]),
+                FloatReduceMeanTemplate.referenceTemplate, ClusterTransformer)
+    for integer_type in SignedIntegerDataTypes
+    for float_type in FloatDataTypes
 ]
 
 PULPReduceSumBindings = [

Deeploy/Targets/PULPOpen/Deployer.py

@@ -15,6 +15,7 @@
 from Deeploy.DeeployTypes import ConstantBuffer, DeploymentPlatform, NodeTemplate, TopologyOptimizer, VariableBuffer
 from Deeploy.Targets.Generic.TopologyOptimizationPasses.Passes import ReshapeConstOptPass, TransposeConstOptPass, \
     TransposeMergePass, TransposeNoPermOptPass, TransposeSplitPass
+from Deeploy.Targets.PULPOpen.Platform import PULPClusterEngine
 from Deeploy.Targets.PULPOpen.TopologyOptimizationPasses.Passes import RQAddTransposeSquashPass
 
 _L3AllocTemplate = NodeTemplate("""
@@ -63,19 +64,32 @@ def __init__(self,
 
         self.extNameCount = 0
 
-    def bind(self):
+    def annotateNCores(self) -> None:
+        for layer in self.layerBinding.values():
+            node = layer.node
+            engine = self._selectEngine(node)
+            opRepr = layer.mapper.parser.operatorRepresentation
+            if isinstance(engine, PULPClusterEngine):
+                opRepr["n_cores"] = engine.n_cores
+
+    def bind(self) -> bool:
         # SCHEREMO: THIS IS A STOP GAP SOLUTION. DONT REUSE. I MEAN IT. I WILL FIND YOU.
         # SCHEREMO: The BindingOptimizationPass system is fairly fragile;
         # it was designed this way because implementing further topology optimizations after
         # parsing is very involved. If there are further use-cases, we should consider making this effort,
         # but if there is only very few cases, this solution is okay.
         autoTransposePass = AutoTransposeMergePass()
         #self.ctxt, self.layerBinding = autoTransposePass.apply(self.ctxt, self.graph, self.layerBinding)
+
+        # LMACAN: THIS IS A STOP GAP SOLUTION. DONT REUSE. I MEAN IT. I WILL FIND YOU.
+        self.annotateNCores()
+
         # SCHEREMO: THIS IS A STOP GAP SOLUTION. DONT REUSE. I MEAN IT. I WILL FIND YOU.
-        ret = super().bind()
-        if ret:
-            self.ctxt.hoistGlobalDefinition("cluster_dev", "extern struct pi_device cluster_dev;")
-        return ret
+        if not super().bind():
+            return False
+
+        self.ctxt.hoistGlobalDefinition("cluster_dev", "extern struct pi_device cluster_dev;")
+        return True
 
     def _l3ConstBuffer(self) -> List[VariableBuffer]:
         return [