Incorporate #203 and #204 (actually!)

Xilinx · Jul 26, 2024 · db75314 · db75314
2 parents f4a2bd5 + 38f9bb1
commit db75314
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Showing 5 changed files with 40 additions and 46 deletions.
diff --git a/lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp b/lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp
@@ -8708,40 +8708,6 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %1 = torch.prim.TupleConstruct %0, %int11 : !torch.int, !torch.int -> !torch.tuple<int, int>\n"
 "    return %1 : !torch.tuple<int, int>\n"
 "  }\n"
-"  func.func @\"__torch_mlir_dtype_fn.aten.fake_quantize_per_tensor_affine\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.float, %arg2: !torch.int, %arg3: !torch.int, %arg4: !torch.int) -> !torch.int {\n"
-"    %int15 = torch.constant.int 15\n"
-"    %none = torch.constant.none\n"
-"    %str = torch.constant.str \"AssertionError: \"\n"
-"    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
-"    %1 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.is_float_dtype(%0#1) : (!torch.int) -> !torch.bool\n"
-"    torch.prim.If %1 -> () {\n"
-"      torch.prim.If.yield\n"
-"    } else {\n"
-"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
-"      torch.prim.If.yield\n"
-"    }\n"
-"    %2 = torch.aten.ne.int %0#1, %int15 : !torch.int, !torch.int -> !torch.bool\n"
-"    torch.prim.If %2 -> () {\n"
-"      torch.prim.If.yield\n"
-"    } else {\n"
-"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
-"      torch.prim.If.yield\n"
-"    }\n"
-"    return %0#1 : !torch.int\n"
-"  }\n"
-"  func.func @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.is_float_dtype(%arg0: !torch.int) -> !torch.bool {\n"
-"    %0 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.all_float_dtypes() : () -> !torch.list<int>\n"
-"    %1 = torch.aten.__contains__.int_list %0, %arg0 : !torch.list<int>, !torch.int -> !torch.bool\n"
-"    return %1 : !torch.bool\n"
-"  }\n"
-"  func.func @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.all_float_dtypes() -> !torch.list<int> {\n"
-"    %int7 = torch.constant.int 7\n"
-"    %int6 = torch.constant.int 6\n"
-"    %int15 = torch.constant.int 15\n"
-"    %int5 = torch.constant.int 5\n"
-"    %0 = torch.prim.ListConstruct %int5, %int15, %int6, %int7 : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>\n"
-"    return %0 : !torch.list<int>\n"
-"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.linspace\"(%arg0: !torch.float, %arg1: !torch.float, %arg2: !torch.int, %arg3: !torch.optional<int>, %arg4: !torch.optional<int>, %arg5: !torch.optional<Device>, %arg6: !torch.optional<bool>) -> !torch.list<int> {\n"
 "    %0 = torch.prim.ListConstruct %arg2 : (!torch.int) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
@@ -9840,6 +9806,40 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    return %0#1 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.fake_quantize_per_tensor_affine\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.float, %arg2: !torch.int, %arg3: !torch.int, %arg4: !torch.int) -> !torch.int {\n"
+"    %int15 = torch.constant.int 15\n"
+"    %none = torch.constant.none\n"
+"    %str = torch.constant.str \"AssertionError: \"\n"
+"    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    %1 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.is_float_dtype(%0#1) : (!torch.int) -> !torch.bool\n"
+"    torch.prim.If %1 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
+"    %2 = torch.aten.ne.int %0#1, %int15 : !torch.int, !torch.int -> !torch.bool\n"
+"    torch.prim.If %2 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
+"    return %0#1 : !torch.int\n"
+"  }\n"
+"  func.func @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.is_float_dtype(%arg0: !torch.int) -> !torch.bool {\n"
+"    %0 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.all_float_dtypes() : () -> !torch.list<int>\n"
+"    %1 = torch.aten.__contains__.int_list %0, %arg0 : !torch.list<int>, !torch.int -> !torch.bool\n"
+"    return %1 : !torch.bool\n"
+"  }\n"
+"  func.func @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.all_float_dtypes() -> !torch.list<int> {\n"
+"    %int7 = torch.constant.int 7\n"
+"    %int6 = torch.constant.int 6\n"
+"    %int15 = torch.constant.int 15\n"
+"    %int5 = torch.constant.int 5\n"
+"    %0 = torch.prim.ListConstruct %int5, %int15, %int6, %int7 : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>\n"
+"    return %0 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.cosh\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    %1 = call @__torch__._get_dtype_of_floating_point_op(%0#1) : (!torch.int) -> !torch.int\n"

diff --git a/projects/pt1/e2e_testing/main.py b/projects/pt1/e2e_testing/main.py
@@ -97,7 +97,11 @@ def main():
     if args.config == "linalg":
         config = LinalgOnTensorsBackendTestConfig(RefBackendLinalgOnTensorsBackend())
         xfail_set = LINALG_XFAIL_SET
-        crashing_set = set(["ConvolutionModule2DTranspose_basic"])
+        # Out of bounds access
+        crashing_set = set(["ConvolutionModule2DTranspose_basic",
+                            "Conv_Transpose2dModule_basic",
+                            "ConvolutionModule2DTransposeStrided_basic",
+                            "ConvolutionModule2DTransposeStridedStatic_basic"])
     elif args.config == "stablehlo":
         config = StablehloBackendTestConfig(LinalgOnTensorsStablehloBackend())
         xfail_set = all_test_unique_names - STABLEHLO_PASS_SET

diff --git a/projects/pt1/e2e_testing/xfail_sets.py b/projects/pt1/e2e_testing/xfail_sets.py
@@ -19,8 +19,6 @@
     "RepeatInterleaveFillModule_basic",
     # tm_tensor.scatter' op mismatch in shape of indices and update value at dim#0
     "IndexPutImpl2DNoneIndexBroadcastStaticModule_basic",
-    # No lowering available
-    "FakeQuantizePerTensorAffineCachemaskModule_basic",
     # Lowering Torch Backend IR -> Linalg-on-Tensors Backend IR failed
     # 'linalg.depthwise_conv_2d_nchw_chw' op inferred input/output operand #1 has shape's dimension #0 to be 4, but found 8
     "Conv2dWithPaddingDilationStrideStaticModule_depthwise_multiplier",
@@ -297,8 +295,6 @@
     # failed to legalize operation 'torch.constant.int'
     "RepeatInterleaveStaticModule_basic",
 
-    # No lowering to linalg
-    "FakeQuantizePerTensorAffineCachemaskModule_basic",
 
     # AssertionError: Unregistered operation: torch.aten._unsafe_index_put
     "UnsafeIndexPutHackedTwin1DFloatNonAccumulateModule_basic",
@@ -593,6 +589,7 @@
     "EyeStaticModule_basic",
     "EqIntModule_basic",
     "ExpandAsIntModule_basic",
+    "FakeQuantizePerTensorAffineCachemaskModule_basic",
     "Fill_TensorFloat64WithFloat32Static_basic",
     "Fill_TensorFloat64WithFloat32_basic",
     "Fill_TensorFloat64WithFloat64_basic",

diff --git a/projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/abstract_interp_lib_gen.py b/projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/abstract_interp_lib_gen.py
@@ -1275,12 +1275,6 @@ def aten〇fake_quantize_per_tensor_affine_cachemask〡shape(self: List[int], sc
 def aten〇fake_quantize_per_tensor_affine_cachemask〡dtype(self_rank_dtype: Tuple[int, int], scale: float, zero_point: int, quant_min: int, quant_max: int) -> Tuple[int, int]:
     return (self_rank_dtype[1], torch.bool)
 
-def aten〇fake_quantize_per_tensor_affine〡shape(self: List[int], scale: float, zero_point: int, quant_min: int, quant_max: int) -> List[int]:
-    return upstream_shape_functions.unary(self)
-
-def aten〇fake_quantize_per_tensor_affine〡dtype(self_rank_dtype: Tuple[int, int], scale: float, zero_point: int, quant_min: int, quant_max: int) -> int:
-    return self_rank_dtype[1]
-
 def aten〇linspace〡shape(start: float, end: float, steps: int, dtype: Optional[int] = None, layout: Optional[int] = None, device: Optional[device] = None, pin_memory: Optional[bool] = None) -> List[int]:
     return [steps]
 

diff --git a/projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/torch_ods_gen.py b/projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/torch_ods_gen.py
@@ -840,7 +840,6 @@ def emit_with_mutating_variants(key, **kwargs):
     emit("aten::ScalarImplicit : (Tensor) -> (Scalar)", has_canonicalizer=True)
 
     emit("aten::fake_quantize_per_tensor_affine_cachemask : (Tensor, float, int, int, int) -> (Tensor, Tensor)")
-    emit("aten::fake_quantize_per_tensor_affine : (Tensor, float, int, int, int) -> (Tensor)")
 
     # backprop ops
     emit("aten::_softmax_backward_data : (Tensor, Tensor, int, int) -> (Tensor)")