Integrate LLVM at llvm/llvm-project@6ae7b73 (#392)

Updates LLVM usage to match llvm/llvm-project@6ae7b73 Further updates
the StableHLO submodule to openxla/stablehlo@2f2520b. In addition, TF is
upgraded to a recent tf-nightly version.

README.md

@@ -73,6 +73,7 @@ The `emitc-opt` tool supports the following options:
 
 | option                                     |                                                                          |
 | :----------------------------------------- |:------------------------------------------------------------------------ |
+| `--convert-scf-to-emitc`                   | Convert SCF dialect to EmitC dialect, maintaining structured control flow|
 | `--convert-stablehlo-region-ops-to-emitc ` | Convert StableHLO operations containing regions to EmitC dialect.        |
 | `--convert-stablehlo-to-emitc `            | Convert from StableHLO dialect to EmitC dialect.                         |
 | `--convert-arith-to-emitc `                | Convert arith dialect to EmitC dialect, replacing IndexCastOp.           |

build_tools/llvm_version.txt

@@ -1 +1 @@
-79e96b2457fe4e1586effc36aab657c508c122cf
+6ae7b735dbd50eb7ade1573a86d037a2943e679c

docs/scf-op-coverage.md

@@ -4,10 +4,10 @@ SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 -->
 # SCF Op Coverage
 
-The table below shows the supported SCF ops.
+The table below shows the SCF ops, supported via `--convert-scf-to-emitc` **upstream** conversions.
 
 | op                    | supported          | comment |
 | :-------------------- |:------------------:| :------ |
-| for                   | :white_check_mark: | via `emitc-translate` |
-| if                    | :white_check_mark: | via `emitc-translate` |
-| yield                 | :white_check_mark: | via `emitc-translate` |
+| for                   | :heavy_check_mark: | |
+| if                    | :heavy_check_mark: | |
+| yield                 | :white_check_mark: | only as part of lowering `for` and `if` ops |

scripts/generate_testscases.py

@@ -32,7 +32,7 @@ def generate_example(model_input):
 
         dtype = model_input.dtype
 
-        if dtype == np.float32:
+        if dtype == "float32":
             return np.random.uniform(low=0.0, high=1.0,
                                      size=shape).astype(np.float32)
         else:

scripts/requirements.txt

@@ -1,3 +1,3 @@
-tf-nightly==2.15.0.dev20230828
-ml_dtypes==0.2.0
+tf-nightly==2.16.0.dev20231102
+ml_dtypes==0.3.1
 wrapt==1.14.1

test/Conversion/tosa-to-emitc.mlir

@@ -147,47 +147,47 @@ func.func @test_logical_left_shift(%arg0: tensor<13x21x1xi32>, %arg1: tensor<13x
 // MulOp: no broadcast
 func.func @test_mul10(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
   // CHECK: emitc.call "emitc::tosa::mul"(%arg0, %arg1) : (tensor<13x21x3xf32>, tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
-  %0 = "tosa.mul"(%arg0, %arg1)  { shift = 0 : i32 } : (tensor<13x21x3xf32>, tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
+  %0 = "tosa.mul"(%arg0, %arg1)  { shift = 0 : i8 } : (tensor<13x21x3xf32>, tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
   return %0 : tensor<13x21x3xf32>
 }
 
 // MulOp: First operand needs to be broadcasted
 func.func @test_mul1(%arg0: tensor<13x1x3xi32>, %arg1: tensor<13x21x3xi32>) -> tensor<13x21x3xi32> {
   // CHECK: emitc.call "emitc::broadcast_in_dim"(%arg0) {args = [0 : index, dense<[0, 1, 2]> : tensor<3xi64>], template_args = [tensor<13x21x3xi32>]} : (tensor<13x1x3xi32>) -> tensor<13x21x3xi32>
-  // CHECK: emitc.call "emitc::tosa::mul"(%0, %arg1) {args = [0 : index, 1 : index, 1 : i32]} : (tensor<13x21x3xi32>, tensor<13x21x3xi32>) -> tensor<13x21x3xi32>
-  %0 = "tosa.mul"(%arg0, %arg1)  { shift = 1 : i32 } : (tensor<13x1x3xi32>, tensor<13x21x3xi32>) -> tensor<13x21x3xi32>
+  // CHECK: emitc.call "emitc::tosa::mul"(%0, %arg1) {args = [0 : index, 1 : index, 1 : i8]} : (tensor<13x21x3xi32>, tensor<13x21x3xi32>) -> tensor<13x21x3xi32>
+  %0 = "tosa.mul"(%arg0, %arg1)  { shift = 1 : i8 } : (tensor<13x1x3xi32>, tensor<13x21x3xi32>) -> tensor<13x21x3xi32>
   return %0 : tensor<13x21x3xi32>
 }
 
 // MulOp: Second operand needs to be broadcasted
 func.func @test_mul2(%arg0: tensor<13x21x3xi32>, %arg1: tensor<13x1x3xi32>) -> tensor<13x21x3xi32> {
   // CHECK: emitc.call "emitc::broadcast_in_dim"(%arg1) {args = [0 : index, dense<[0, 1, 2]> : tensor<3xi64>], template_args = [tensor<13x21x3xi32>]} : (tensor<13x1x3xi32>) -> tensor<13x21x3xi32>
-  // CHECK: emitc.call "emitc::tosa::mul"(%arg0, %0) {args = [0 : index, 1 : index, 1 : i32]} : (tensor<13x21x3xi32>, tensor<13x21x3xi32>) -> tensor<13x21x3xi32>
-  %0 = "tosa.mul"(%arg0, %arg1)  { shift = 1 : i32 } : (tensor<13x21x3xi32>, tensor<13x1x3xi32>) -> tensor<13x21x3xi32>
+  // CHECK: emitc.call "emitc::tosa::mul"(%arg0, %0) {args = [0 : index, 1 : index, 1 : i8]} : (tensor<13x21x3xi32>, tensor<13x21x3xi32>) -> tensor<13x21x3xi32>
+  %0 = "tosa.mul"(%arg0, %arg1)  { shift = 1 : i8 } : (tensor<13x21x3xi32>, tensor<13x1x3xi32>) -> tensor<13x21x3xi32>
   return %0 : tensor<13x21x3xi32>
 }
 
 // MulOp: Second operand needs to be broadcasted + expanded to two dimensions
 func.func @test_mul3(%arg0: tensor<21x3xi32>, %arg1: tensor<3xi32>) -> tensor<21x3xi32> {
   // CHECK: emitc.call "emitc::broadcast_in_dim"(%arg1) {args = [0 : index, dense<1> : tensor<1xi64>], template_args = [tensor<21x3xi32>]} : (tensor<3xi32>) -> tensor<21x3xi32>
-  // CHECK: emitc.call "emitc::tosa::mul"(%arg0, %0) {args = [0 : index, 1 : index, 3 : i32]} : (tensor<21x3xi32>, tensor<21x3xi32>) -> tensor<21x3xi32>
-  %0 = "tosa.mul"(%arg0, %arg1)  { shift = 3 : i32 } : (tensor<21x3xi32>, tensor<3xi32>) -> tensor<21x3xi32>
+  // CHECK: emitc.call "emitc::tosa::mul"(%arg0, %0) {args = [0 : index, 1 : index, 3 : i8]} : (tensor<21x3xi32>, tensor<21x3xi32>) -> tensor<21x3xi32>
+  %0 = "tosa.mul"(%arg0, %arg1)  { shift = 3 : i8 } : (tensor<21x3xi32>, tensor<3xi32>) -> tensor<21x3xi32>
   return %0 : tensor<21x3xi32>
 }
 
 // MulOp: Second operand needs to be broadcasted + expanded to three dimensions
 func.func @test_mul4(%arg0: tensor<13x21x3xi32>, %arg1: tensor<3xi32>) -> tensor<13x21x3xi32> {
   // CHECK: emitc.call "emitc::broadcast_in_dim"(%arg1) {args = [0 : index, dense<2> : tensor<1xi64>], template_args = [tensor<13x21x3xi32>]} : (tensor<3xi32>) -> tensor<13x21x3xi32>
-  // CHECK: emitc.call "emitc::tosa::mul"(%arg0, %0) {args = [0 : index, 1 : index, 1 : i32]} : (tensor<13x21x3xi32>, tensor<13x21x3xi32>) -> tensor<13x21x3xi32>
-  %0 = "tosa.mul"(%arg0, %arg1)  { shift = 1 : i32 } : (tensor<13x21x3xi32>, tensor<3xi32>) -> tensor<13x21x3xi32>
+  // CHECK: emitc.call "emitc::tosa::mul"(%arg0, %0) {args = [0 : index, 1 : index, 1 : i8]} : (tensor<13x21x3xi32>, tensor<13x21x3xi32>) -> tensor<13x21x3xi32>
+  %0 = "tosa.mul"(%arg0, %arg1)  { shift = 1 : i8 } : (tensor<13x21x3xi32>, tensor<3xi32>) -> tensor<13x21x3xi32>
   return %0 : tensor<13x21x3xi32>
 }
 
 // MulOp: Second two dimensional operand needs to be broadcasted + expanded to four dimensions
 func.func @test_mul5(%arg0: tensor<2x13x21x3xi32>, %arg1: tensor<21x3xi32>) -> tensor<2x13x21x3xi32> {
   // CHECK: emitc.call "emitc::broadcast_in_dim"(%arg1) {args = [0 : index, dense<[2, 3]> : tensor<2xi64>], template_args = [tensor<2x13x21x3xi32>]} : (tensor<21x3xi32>) -> tensor<2x13x21x3xi32>
-  // CHECK: emitc.call "emitc::tosa::mul"(%arg0, %0) {args = [0 : index, 1 : index, 5 : i32]} : (tensor<2x13x21x3xi32>, tensor<2x13x21x3xi32>) -> tensor<2x13x21x3xi32>
-  %0 = "tosa.mul"(%arg0, %arg1)  { shift = 5 : i32 } : (tensor<2x13x21x3xi32>, tensor<21x3xi32>) -> tensor<2x13x21x3xi32>
+  // CHECK: emitc.call "emitc::tosa::mul"(%arg0, %0) {args = [0 : index, 1 : index, 5 : i8]} : (tensor<2x13x21x3xi32>, tensor<2x13x21x3xi32>) -> tensor<2x13x21x3xi32>
+  %0 = "tosa.mul"(%arg0, %arg1)  { shift = 5 : i8 } : (tensor<2x13x21x3xi32>, tensor<21x3xi32>) -> tensor<2x13x21x3xi32>
   return %0 : tensor<2x13x21x3xi32>
 }