[Sync] distributed runtime support and many updates (#83)

* [Sync] distributed runtime support and many updates

* update torch-mlir

* fix multi process

---------

Co-authored-by: Zhekun Zhang <[email protected]>

NOTICE

@@ -1355,3 +1355,80 @@ SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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compiler/doc/byteir_mhlo_custom_call.md

@@ -42,13 +42,15 @@ If an op is frontend-specific, it uses a frontend-specific prefix, such as `tf`
 
 Further needed infomation for a given coarse-grained op are encoded in a dictionary attribute, called `byteir_attrs`, which includes all named attributes. 
 
-```Op Attribute: byteir_attrs = {approximate = "none"} or byteir_attrs = {} of if none```
+**Op Attribute**:
+  * ```byteir_attrs = {approximate = "none"}``` or ```byteir_attrs = {}``` if no attribute
+  * ```axis``` attribute must be positive
 
 ### byteir.layer_norm
 - Operands:
   - input: Tensor
-  - weight: Tensor
-  - bias: Tensor
+  - weight: Tensor (shape should be same as axis of input tensor)
+  - bias: Tensor (shape should be same as axis of input tensor)
 - Attrs
   - epsilon: F64Attr
   - axis: I64ArrayAttr
@@ -138,6 +140,12 @@ Further needed infomation for a given coarse-grained op are encoded in a diction
 %0 = "mhlo.custom_call"(%arg0) {call_target_name = "byteir.erf", has_side_effect = false} : (tensor<?x64xf32>) -> tensor<?x64xf32>
 ```
 
+### byteir.addn
+- Operands:
+  - inputs: Variadic\<Tensor>
+- Results:
+  - outputs: Tensor
+
 ### byteir.one_hot
 - Operands:
   - indices: IntTensor
@@ -149,13 +157,20 @@ Further needed infomation for a given coarse-grained op are encoded in a diction
 - Results:
   - output: Tensor (ElementType same as on_value and off_value)
 
+### byteir.repeat
+- Operands:
+  - input: Tensor
+  - repeats: Int32/Int64 Tensor
+- Results:
+  - output: Tensor (ElementType same as input)
+
 ### byteir.quantize
 - Operands:
   - input: FloatTensor
   - scale: FloatTensor (rank=0 for per-tensor quantization, or rank=1 for per-channel quantization)
   - zero_point: Int8/Int16/Uint8/Uint16 Tensor (shape same as scale)
 - Attrs
-  - axis: I64Attr (Optional, required only for per-channel quantization)
+  - axis: Optional\<I64Attr> (required only for per-channel quantization)
 - Results:
   - output: Int8/Int16/Uint8/Uint16 Tensor (type same as zero_point)
 
@@ -165,7 +180,7 @@ Further needed infomation for a given coarse-grained op are encoded in a diction
   - scale: FloatTensor (rank=0 for per-tensor dequantization, or rank=1 for per-channel dequantization)
   - zero_point: Int8/Int16/Uint8/Uint16 Tensor (shape same as scale, type same as input)
 - Attrs
-  - axis: I64Attr (Optional, channel axis index, required only for per-channel dequantization)
+  - axis: Optional\<I64Attr> (channel axis index, required only for per-channel dequantization)
 - Results:
   - output: FloatTensor
 
@@ -220,3 +235,39 @@ Further needed infomation for a given coarse-grained op are encoded in a diction
 %shape = shape.shape_of %arg0 : tensor<3xindex>
 %0 = "mhlo.custom_call"(%low, %high, %seed, %offset, %shape) {call_target_name = "byteir.rng_uniform", has_side_effect = false} : (tensor<f32>, tensor<f32>, tensor<i64>, tensor<i64>, tensor<3xindex>) -> tensor<?x?x?xf32>
 ```
+
+### byteir.flash_attn_fwd
+- Operands:
+  - q: Tensor
+  - k: Tensor
+  - v: Tensor
+- Attrs:
+  - dropout_p: FloatAttr
+  - softmax_scale: FloatAttr
+  - causal: BoolAttr
+  - return_softmax: BoolAttr
+- Results:
+  - output: Tensor
+  - softmax_lse: Tensor
+  - softmax_return: Tensor
+  - rng: Tensor
+
+### byteir.flash_attn_bwd
+- Operands:
+  - dout: Tensor
+  - q: Tensor
+  - k: Tensor
+  - v: Tensor
+  - out: Tensor
+  - softmax_lse: Tensor
+  - rng_state: Tensor
+- Attrs:
+  - dropout_p: FloatAttr
+  - softmax_scale: FloatAttr
+  - causal: BoolAttr
+- Results:
+  - dq: Tensor
+  - dk: Tensor
+  - dv: Tensor
+  - d_softmax: Tensor
+  - dq_accum: Tensor

compiler/include/byteir/Dialect/Ace/AceOps.td

@@ -48,13 +48,27 @@ def Ace_ConstOp : Ace_Op<"constant", [
   let builders = [
     OpBuilder<(ins "Attribute":$value)>];
 
-  // FIXME: let this op only has generic format for golden ref calculate.
+  // FIXME: let this op only has generic format for loading without register ace dialect.
   // let assemblyFormat = "attr-dict $value";
   let hasFolder = 1;
   let hasVerifier = 0;
 }
 
 
+def Ace_ReshapeOp : Ace_Op<"reshape", [Pure, SameOperandsAndResultElementType]> {
+  let summary = "Reshape operation";
+  let description = [{
+    Performs reshape of `operand` tensor to a `result` tensor, like `mhlo.reshape`.
+  }];
+
+  let arguments = (ins AnyTensor:$operand);
+
+  let results = (outs Ace_StaticShapeTensor);
+  let hasVerifier = 1;
+  // note: let this op only has generic format for loading without register ace dialect.
+}
+
+
 def Ace_ActivateOp : Ace_Op<"activate", [Pure]> {
   let summary = "Activate operation";
 

compiler/include/byteir/Dialect/mhlo/Transforms/CanonicalizeExt.h

@@ -32,6 +32,13 @@ namespace mhlo {
 // fold multi op with zero
 void populateFoldMultiplyZeroPattern(RewritePatternSet &patterns);
 
+// fold large binary Op
+void populateFoldLargeBinaryOpPatterns(RewritePatternSet &patterns);
+
+// fold benefical convert with constant
+void populateFoldBeneficialConstantConvertOpPattern(
+    RewritePatternSet &patterns);
+
 // populate canonicalizeExt patterns
 void populateCanonicalizeExtPatterns(RewritePatternSet &patterns,
                                      MLIRContext *context,

compiler/include/byteir/Dialect/mhlo/Util/CustomCallUtil.h

@@ -81,6 +81,10 @@ constexpr llvm::StringRef getDequantizeName() {
   return CUSTOM_CALL_NAME_PREFIX "dequantize";
 }
 
+constexpr llvm::StringRef getResizeName() {
+  return CUSTOM_CALL_NAME_PREFIX "resize";
+}
+
 constexpr llvm::StringRef getRngUniformName() {
   return CUSTOM_CALL_NAME_PREFIX "rng_uniform";
 }

compiler/include/byteir/Dialect/mhlo/Util/Util.h

@@ -41,8 +41,8 @@ enum class NamedLayout : uint32_t {
 
 inline std::string stringifyEnum(NamedLayout layout) {
   switch (layout) {
-  case NamedLayout::UNKNOWN:
-    return "UNKNOWN";
+  // case NamedLayout::UNKNOWN:
+  //   return "UNKNOWN";
   case NamedLayout::NHWC:
     return "NHWC";
   case NamedLayout::NDHWC:

compiler/include/byteir/Target/Cpp/CppEmitter.h

@@ -29,6 +29,8 @@ class CppEmitter {
 public:
   explicit CppEmitter(llvm::raw_ostream &os, bool declareVariablesAtTop);
 
+  virtual ~CppEmitter() = default;
+
   /// Emits attribute or returns failure.
   virtual mlir::LogicalResult emitAttribute(mlir::Location loc,
                                             mlir::Attribute attr);

compiler/include/byteir/Transforms/GraphClusteringAlgo.h

@@ -26,6 +26,7 @@ enum class GraphClusteringAlgo : uint32_t {
   kFallback = 0,
   kTopDown = 1,
   kBottomUp = 2,
+  kGreedy = 3,
 };
 
 } // namespace mlir

compiler/include/byteir/Transforms/Passes.td

@@ -207,7 +207,8 @@ def GraphClusteringByDevice : Pass<"graph-clustering-by-device", "ModuleOp"> {
            [{llvm::cl::values(
               clEnumValN(mlir::GraphClusteringAlgo::kFallback, "Fallback", "Fallback clustering algorithm"),
               clEnumValN(mlir::GraphClusteringAlgo::kTopDown, "TopDown", "Create and merge device cluster progressively from top to bottom"),
-              clEnumValN(mlir::GraphClusteringAlgo::kBottomUp, "BottomUp", "Create and merge device cluster progressively from bottom to top")
+              clEnumValN(mlir::GraphClusteringAlgo::kBottomUp, "BottomUp", "Create and merge device cluster progressively from bottom to top"),
+              clEnumValN(mlir::GraphClusteringAlgo::kGreedy, "Greedy", "Choose largest subgraph from BottomUp or TopDown strategy")
             )}]>
   ];
 }

compiler/lib/Conversion/HloToByreTensor/HloToByreTensor.cpp

@@ -129,16 +129,16 @@ class ConvertConstLikeOp : public OpConversionPattern<ConstOp> {
   }
 };
 
-class ConvertReshapeOp : public OpConversionPattern<mhlo::ReshapeOp> {
+template <typename OP> class ConvertReshapeOp : public OpConversionPattern<OP> {
 public:
-  using OpConversionPattern::OpConversionPattern;
+  using OpConversionPattern<OP>::OpConversionPattern;
 
   LogicalResult
-  matchAndRewrite(mhlo::ReshapeOp op, mhlo::ReshapeOp::Adaptor adaptor,
+  matchAndRewrite(OP op, typename OP::Adaptor adaptor,
                   ConversionPatternRewriter &rewriter) const final {
     auto operand = adaptor.getOperand();
-    auto operandType = operand.getType().cast<ShapedType>();
-    auto resultType = op.getType().cast<ShapedType>();
+    auto operandType = operand.getType().template cast<ShapedType>();
+    auto resultType = op.getType().template cast<ShapedType>();
 
     if (!operandType.hasStaticShape() || !resultType.hasStaticShape())
       return failure();
@@ -749,9 +749,10 @@ void mlir::populateHloToByreTensorPattern(
                ConvertSelectAndScatterOpToByrePattern>(patterns.getContext(),
                                                        appendArgTypes);
 
-  patterns.add<ConvertConstLikeOp<mhlo::ConstantOp>,
-               ConvertConstLikeOp<ace::ConstOp>, ConvertReshapeOp,
-               ConvertSliceOp, ConvertConcatenateOp>(patterns.getContext());
+  patterns.add<
+      ConvertConstLikeOp<mhlo::ConstantOp>, ConvertConstLikeOp<ace::ConstOp>,
+      ConvertReshapeOp<mhlo::ReshapeOp>, ConvertReshapeOp<ace::ReshapeOp>,
+      ConvertSliceOp, ConvertConcatenateOp>(patterns.getContext());
 }
 
 std::unique_ptr<OperationPass<func::FuncOp>>