[TorchToLinalg] add support for quantized group conv (#3341)

This addresses 7 of the model failures I'm seeing in the test suite. See
[Shark-Turbine issue
#566](nod-ai/SHARK-ModelDev#566).

Need the op ```linalg.conv_2d_ngchw_gfchw_q``` to be added upstream
before merging this. See [llvm-project PR #92136
](llvm/llvm-project#92136).

A small additional expansion to operand quantization is included in this
patch to address a model failure that occurs when unblocking the
quantized group convolutions in one of these onnx models.

lib/Conversion/TorchToLinalg/Linear.cpp

@@ -829,7 +829,7 @@ class ConvertAtenConvolutionOp : public OpConversionPattern<AtenConvolutionOp> {
           op, "lhs and rhs of convolution must either be both int or fp");
     }
 
-    if (inputZp && weightZp && !isa<Torch::NoneType>(bias.getType())) {
+    if (inputZp && !isa<Torch::NoneType>(bias.getType())) {
       auto biasDTy = cast<RankedTensorType>(bias.getType()).getElementType();
       if (!biasDTy.isInteger(32)) {
         return rewriter.notifyMatchFailure(
@@ -1123,7 +1123,7 @@ class ConvertAtenConvolutionOp : public OpConversionPattern<AtenConvolutionOp> {
     // - grouped 1d-3d
     // - grouped 1d-3d (quantized)
     // - ungrouped 1d-3d
-    if (groupSize == 1 && !inputZp && !weightZp) {
+    if (groupSize == 1 && !inputZp) {
       switch (numSpatialDims) {
       case 1:
         conv = rewriter
@@ -1164,7 +1164,7 @@ class ConvertAtenConvolutionOp : public OpConversionPattern<AtenConvolutionOp> {
       return success();
     }
 
-    if (groupSize == 1 && inputZp && weightZp) {
+    if (groupSize == 1 && inputZp) {
       // The quantized version uses a different channel ordering so we need to
       // permute the tensors in order to use the existing path. We should
       // eventually directly support this channel ordering.
@@ -1224,10 +1224,6 @@ class ConvertAtenConvolutionOp : public OpConversionPattern<AtenConvolutionOp> {
       return success();
     }
 
-    if (inputZp || weightZp)
-      return rewriter.notifyMatchFailure(
-          op, "unimplemented: quantized grouped convolutions");
-
     if (numSpatialDims != 2)
       return rewriter.notifyMatchFailure(
           op, "unimplemented: only 2D grouped convolution supported");
@@ -1238,7 +1234,7 @@ class ConvertAtenConvolutionOp : public OpConversionPattern<AtenConvolutionOp> {
     auto weightShape = makeShapeTorchCompatible(
         cast<RankedTensorType>(weight.getType()).getShape());
     if (weightShape[0] != kUnknownSize && inShape[1] == groupSize &&
-        weightShape[0] % inShape[1] == 0 && weightShape[1] == 1) {
+        weightShape[0] % inShape[1] == 0 && weightShape[1] == 1 && !inputZp) {
       // Collapse weight shape
       SmallVector<ReassociationIndices, 4> collapsedDims = {{0, 1}, {2}, {3}};
       SmallVector<int64_t> collapsedShape{
@@ -1325,13 +1321,22 @@ class ConvertAtenConvolutionOp : public OpConversionPattern<AtenConvolutionOp> {
     auto expandOutputTensor = expandGroups(outputTensor, 1);
 
     // TODO: add 1D and 3D case
-    conv = rewriter
-               .create<linalg::Conv2DNgchwGfchwOp>(
-                   loc, expandOutputTensor.getResultType(),
-                   ValueRange{paddedInputExpanded, weightExpanded},
-                   expandOutputTensor.getResult(), stridesAttr, dilationAttr)
-               .getResult(0);
-
+    if (!inputZp) {
+      conv = rewriter
+                 .create<linalg::Conv2DNgchwGfchwOp>(
+                     loc, expandOutputTensor.getResultType(),
+                     ValueRange{paddedInputExpanded, weightExpanded},
+                     expandOutputTensor.getResult(), stridesAttr, dilationAttr)
+                 .getResult(0);
+    } else {
+      conv = rewriter
+                 .create<linalg::Conv2DNgchwGfchwQOp>(
+                     loc, expandOutputTensor.getResultType(),
+                     ValueRange{paddedInputExpanded, weightExpanded, inputZp,
+                                weightZp},
+                     expandOutputTensor.getResult(), stridesAttr, dilationAttr)
+                 .getResult(0);
+    }
     conv = rewriter.create<tensor::CollapseShapeOp>(
         loc, outputTensor.getType(), conv,
         expandOutputTensor.getReassociationIndices());

lib/Dialect/Torch/Transforms/FuseQuantizedOps.cpp

@@ -378,7 +378,7 @@ class FuseQuantizedOpsPass : public FuseQuantizedOpsBase<FuseQuantizedOpsPass> {
         QuantizeOperandsPastCommutingOps<AtenConvolutionOp, 5>,
         QuantizeOperandsPastCommutingOps<AtenReluOp, 0>,
         QuantizeOperandsPastCommutingOps<AtenMatmulOp, 2>,
-        QuantizeOperandsPastCommutingOps<AtenMmOp, 2>,
+        QuantizeOperandsPastCommutingOps<AtenMmOp, 4>,
         QuantizeAccumulator<AtenMmOp>, QuantizeAccumulator<AtenMatmulOp>,
         QuantizeResultLikeOperand<AtenReluOp>, QuantizeBias<AtenConvolutionOp>>(
         context);

projects/pt1/e2e_testing/xfail_sets.py

@@ -277,6 +277,7 @@
     "QuantizedReluInt8_basic",
     "QuantizedReluUint8_basic",
     "Conv2dQInt8Module_basic",
+    "Conv2dQInt8Module_grouped",
     "ConvTranspose2DQInt8_basic",
     # Dynamo not supporting conv_tbc
     "ConvTbcModule_basic",
@@ -373,6 +374,7 @@
     "ContainsIntList_False",
     "ContainsIntList_True",
     "Conv2dQInt8Module_basic",
+    "Conv2dQInt8Module_grouped",
     "Conv2dWithPaddingDilationStrideStaticModule_depthwise_multiplier",
     "ConvTbcModule_basic",
     "ConvTranspose2DQInt8_basic",
@@ -543,6 +545,7 @@
     "ContainsIntList_False",
     "ContainsIntList_True",
     "Conv2dQInt8Module_basic",
+    "Conv2dQInt8Module_grouped",
     "ConvTbcModule_basic",
     "ConvTranspose2DQInt8_basic",
     "ConvolutionBackwardModule2DPadded_basic",
@@ -2147,6 +2150,7 @@
     "ElementwiseBitwiseAndScalarInt32Module_basic",
     "ElementwiseBitwiseAndScalarInt8Module_basic",
     "Conv2dQInt8Module_basic",
+    "Conv2dQInt8Module_grouped",
     "ConvTranspose2DQInt8_basic",
 }
 
@@ -2298,6 +2302,7 @@
     "Conv2dModule_basic",
     "Conv2dNoPaddingModule_basic",
     "Conv2dQInt8Module_basic",
+    "Conv2dQInt8Module_grouped",
     "Conv2dWithPaddingDilationStrideModule_basic",
     "Conv2dWithPaddingModule_basic",
     "Conv3dModule_basic",
@@ -2851,6 +2856,7 @@
     "ContainsIntList_True",
     "Conv1dModule_basic",
     "Conv2dQInt8Module_basic",
+    "Conv2dQInt8Module_grouped",
     "Conv2dWithPaddingDilationStrideStaticModule_grouped",
     "Conv2dWithPaddingDilationStrideStaticModule_grouped_multiplier",
     "Conv3dModule_basic",
@@ -3637,6 +3643,7 @@
     "Conv2dModule_basic",
     "Conv2dNoPaddingModule_basic",
     "Conv2dQInt8Module_basic",
+    "Conv2dQInt8Module_grouped",
     "Conv2dWithPaddingDilationStrideModule_basic",
     "Conv2dWithPaddingDilationStrideStaticModule_grouped",
     "Conv2dWithPaddingDilationStrideStaticModule_grouped_multiplier",

projects/pt1/python/torch_mlir_e2e_test/test_suite/conv.py

@@ -1157,7 +1157,8 @@ def ConvTbcModule_basic(module, tu: TestUtils):
 
 
 class Conv2dQInt8Module(torch.nn.Module):
-    def __init__(self):
+    def __init__(self, groups=1):
+        self.groups = groups
         super().__init__()
 
     @export
@@ -1186,7 +1187,7 @@ def forward(self, inputVec, weight, bias):
             stride=[1, 1],
             padding=[0, 0],
             dilation=[1, 1],
-            groups=1,
+            groups=self.groups,
         )
 
 
@@ -1198,13 +1199,12 @@ def Conv2dQInt8Module_basic(module, tu: TestUtils):
     module.forward(inputVec, weight, bias)
 
 
-N = 10
-Cin = 5
-Cout = 7
-Hin = 10
-Win = 8
-Hker = 3
-Wker = 2
+@register_test_case(module_factory=lambda: Conv2dQInt8Module(groups=2))
+def Conv2dQInt8Module_grouped(module, tu: TestUtils):
+    inputVec = tu.randint(2, 8, 7, 8, low=-128, high=127).to(torch.int8)
+    weight = tu.randint(6, 4, 3, 2, low=-128, high=127).to(torch.int8)
+    bias = torch.rand(6)
+    module.forward(inputVec, weight, bias)
 
 
 class ConvTranspose2DQInt8Module(torch.nn.Module):
@@ -1244,6 +1244,13 @@ def forward(self, input, weight, bias):
 
 @register_test_case(module_factory=lambda: ConvTranspose2DQInt8Module())
 def ConvTranspose2DQInt8_basic(module, tu: TestUtils):
+    N = 10
+    Cin = 5
+    Cout = 7
+    Hin = 10
+    Win = 8
+    Hker = 3
+    Wker = 2
     module.forward(
         tu.randint(N, Cin, Hin, Win, low=-128, high=127).to(torch.int8),
         tu.randint(Cin, Cout, Hker, Wker, low=-128, high=127).to(torch.int8),