pytorch · DenisVieriu97 · Oct 26, 2022 · Jan 3, 2023 · Jan 3, 2023 · Jan 3, 2023
diff --git a/aten/src/ATen/native/mps/OperationUtils.h b/aten/src/ATen/native/mps/OperationUtils.h
@@ -43,6 +43,8 @@ MPSDataType getMPSDataType(ScalarType scalar_type);
 MPSDataType getMPSScalarType(ScalarType scalar_type);
 MPSScalar   getMPSScalar(const Scalar& scalar, ScalarType type);
 std::string getMPSTypeString(ScalarType scalar_type);
+NSArray<NSNumber*>* getTensorAxes(const Tensor& t);
+NSArray<NSNumber*>* getTensorAxes(const Tensor& t, at::OptionalIntArrayRef dim);
 std::string getMPSShapeString(MPSShape* shape);
 std::string getTensorsStringKey(const TensorList& tensors, bool use_scalar_value = false);
 std::string getArrayRefString(const IntArrayRef s);
@@ -127,6 +129,13 @@ struct MPSUnaryCachedGraph : public MPSCachedGraph
   MPSGraphTensor *outputTensor_ = nil;
 };
 
+struct MPSBinaryCachedGraph : public MPSCachedGraph
+{
+  MPSBinaryCachedGraph(MPSGraph *graph) : MPSCachedGraph(graph) {}
+  MPSGraphTensor *inputTensor_ = nil;
+  MPSGraphTensor *otherTensor_ = nil;
+  MPSGraphTensor *outputTensor_ = nil;
+};
 
 // TODO: Improve the overall design of MPSGraphCache.
 // https://github.com/pytorch/pytorch/issues/77176

diff --git a/aten/src/ATen/native/mps/OperationUtils.mm b/aten/src/ATen/native/mps/OperationUtils.mm
@@ -87,6 +87,30 @@ MPSDataType getMPSScalarType(ScalarType scalar_type) {
   }
 }
 
+NSArray<NSNumber*>* getTensorAxes(const Tensor& t) {
+  int64_t ndim = t.dim();
+  auto axes = [NSMutableArray<NSNumber*> arrayWithCapacity:ndim];
+  for (const auto i: c10::irange(ndim)) {
+    axes[i] = [NSNumber numberWithInteger:i];
+  }
+  return axes;
+}
+
+NSArray<NSNumber*>* getTensorAxes(const Tensor& t, at::OptionalIntArrayRef dim) {
+  if (dim.has_value() && dim.value().size() != 0) {
+    IntArrayRef dimValues = dim.value();
+    int ndim = dimValues.size();
+    auto axes = [NSMutableArray<NSNumber*> arrayWithCapacity:ndim];
+    for (const auto i: c10::irange(ndim)) {
+      axes[i] = [NSNumber numberWithInteger:dimValues[i]];
+    }
+
+    return axes;
+  }
+
+  return getTensorAxes(t);
+}
+
 std::string getMPSShapeString(MPSShape* shape) {
     std::string str;
     for(NSNumber *elem in shape) {

diff --git a/aten/src/ATen/native/mps/operations/ReduceOps.mm b/aten/src/ATen/native/mps/operations/ReduceOps.mm
@@ -15,6 +15,9 @@
 namespace at {
 namespace native {
 
+typedef MPSGraphTensor* (^NormOpBlock)(mps::MPSBinaryCachedGraph*, MPSGraphTensor*, MPSGraphTensor*);
+#define NormOpFn(graph, primary, secondary) MPSGraphTensor* (mps::MPSBinaryCachedGraph* graph, MPSGraphTensor* primary, MPSGraphTensor* secondary)
+
 enum StdVarType {
   STANDARD_VARIANCE,
   STANDARD_DEVIATION
@@ -34,15 +37,6 @@
 
 using namespace mps;
 
-NSArray<NSNumber*>* getTensorAxes(const Tensor& t) {
-  int64_t ndim = t.dim();
-  auto axes = [NSMutableArray<NSNumber*> arrayWithCapacity:ndim];
-  for (const auto i: c10::irange(ndim)) {
-    axes[i] = [NSNumber numberWithInteger:i];
-  }
-  return axes;
-}
-
 void set_apparent_shapes(NSMutableArray<NSNumber*> * &apparent_out_shape,
                          NSMutableArray<NSNumber*> * &apparent_in_shape,
                          int64_t num_reduce_dims,
@@ -410,19 +404,28 @@ Tensor count_nonzero_mps(const Tensor& self, IntArrayRef dims) {
   reduction_out_mps(input_t, opt_dim, keepdim, dtype, output_t, MPSReductionType::MEAN, "mean_out_mps");
 }
 
-TORCH_IMPL_FUNC(norm_out_mps)
-    (const Tensor& input_tensor,
-     const OptionalScalarRef opt_p,
-     IntArrayRef dim,
-     bool keepdim,
-     const Tensor& output_t) {
+void impl_func_norm_mps(
+  const Tensor& input_tensor,
+  const Tensor& other_tensor,
+  const OptionalScalarRef& opt_p,
+  IntArrayRef dim,
+  bool keepdim,
+  c10::optional<ScalarType> opt_dtype,
+  const Tensor& output_t,
+  bool cdist = false,
+  c10::optional<IntArrayRef> input_broadcasted_shape = c10::nullopt,
+  NormOpBlock normOpBlock = nullptr
+  ) {
+
   if (input_tensor.numel() == 0) {
     return;
   }
 
   auto input_t = (input_tensor.sizes().size() == 0) ? input_tensor.view({1}) : input_tensor;
+  auto in_dtype = opt_dtype.value_or(input_tensor.scalar_type());
+  auto mps_input_dtype = getMPSDataType(in_dtype);
 
-  IntArrayRef input_shape = input_t.sizes();
+  IntArrayRef input_shape = cdist ? input_broadcasted_shape.value() : input_t.sizes();
 
   for (const auto dim_val: dim) {
     auto wrap_dim = maybe_wrap_dim(dim_val, input_shape.size());
@@ -456,6 +459,13 @@ Tensor count_nonzero_mps(const Tensor& self, IntArrayRef dims) {
                       num_output_dims,
                       input_shape,
                       axes);
+
+  NSArray<NSNumber*>* wrappedAxes = mps::getTensorAxes(input_t, dim);
+  if (cdist) {
+    apparent_input_shape  = [mps::getMPSShape(input_tensor.sizes()) mutableCopy];
+    apparent_output_shape = [mps::getMPSShape(output_t.sizes()) mutableCopy];
+  }
+
   if (output_t.numel() == 0) {
     return;
   }
@@ -465,100 +475,210 @@ Tensor count_nonzero_mps(const Tensor& self, IntArrayRef dims) {
   @autoreleasepool {
     NSString* ns_key = [[axes valueForKey:@"description"] componentsJoinedByString:@","];
       string keepdim_info = (keepdim) ? "keepdim=1" : "keepdim=0";
-      string key =  string("norm_out_mps:") + [ns_key UTF8String] + ":" + getMPSTypeString(input_t.scalar_type()) + ":p" + to_string(p) + ":" + keepdim_info;
+      string tensor_key = cdist ? getTensorsStringKey({input_tensor, other_tensor}) : mps::getTensorsStringKey({input_t});
+      string key =  string("norm_out_mps:") + [ns_key UTF8String] + ":" + tensor_key + ":p" + to_string(p) + ":" + keepdim_info;
 
-    auto cachedGraph = cache_->LookUpAs<MPSUnaryCachedGraph>(key);
+    auto cachedGraph = cache_->LookUpAs<MPSBinaryCachedGraph>(key);
 
-    if (!cachedGraph) {
-      cachedGraph = cache_->CreateCachedGraphAs<MPSUnaryCachedGraph>(key, ^ MPSCachedGraph * () {
+    if(!cachedGraph) {
+      cachedGraph = cache_->CreateCachedGraphAs<MPSBinaryCachedGraph>(key, ^ MPSCachedGraph * () {
 
-        MPSUnaryCachedGraph *newCachedGraph = nil;
+        MPSBinaryCachedGraph *newCachedGraph = nil;
 
         @autoreleasepool {
           MPSGraph* mpsGraph = make_mps_graph();
-          newCachedGraph = new MPSUnaryCachedGraph(mpsGraph);
+          newCachedGraph = new MPSBinaryCachedGraph(mpsGraph);
+          newCachedGraph->inputTensor_ = mpsGraphRankedPlaceHolder(mpsGraph, input_tensor);
 
-          MPSGraphTensor* inputTensor = mpsGraphUnrankedPlaceHolder(mpsGraph, getMPSDataType(input_t.scalar_type()));
+          if (cdist) {
+            newCachedGraph->otherTensor_ = mpsGraphRankedPlaceHolder(mpsGraph, other_tensor);
+          }
 
-          MPSGraphTensor *outputTensor = nil;
+          MPSGraphTensor* inputTensor = cdist ? normOpBlock(newCachedGraph, newCachedGraph->inputTensor_, newCachedGraph->otherTensor_) :
+                                                newCachedGraph->inputTensor_;
+          if (opt_dtype.has_value()) {
+            inputTensor = [mpsGraph castTensor:inputTensor
+                                        toType:mps_input_dtype
+                                          name:@"castInputTensor"];
+          }
+
+          MPSGraphTensor *outputTensor;
 
           if (pIsZero) {
               MPSGraphTensor *absoluteTensor = [mpsGraph absoluteWithTensor:inputTensor
                                                                        name:nil];
               MPSGraphTensor *powerValTensor = [mpsGraph constantWithScalar:p
-                                                                   dataType:getMPSDataType(input_t.scalar_type())];
+                                                                   dataType:mps_input_dtype];
               MPSGraphTensor *powerTensor = [mpsGraph powerWithPrimaryTensor:absoluteTensor
                                                              secondaryTensor:powerValTensor
                                                                         name:nil];
               outputTensor = [mpsGraph reductionSumWithTensor:powerTensor
-                                                         axes:axes
+                                                         axes:wrappedAxes
                                                          name:nil];
-          } else if (pIsPosInf) {
+          }
+          else if (pIsPosInf) {
               MPSGraphTensor *absoluteTensor = [mpsGraph absoluteWithTensor:inputTensor
                                                                        name:nil];
               outputTensor = [mpsGraph reductionMaximumWithTensor:absoluteTensor
-                                                             axes:axes
+                                                             axes:wrappedAxes
                                                              name:nil];
-          } else if (pIsNegInf) {
+          }
+          else if (pIsNegInf) {
               MPSGraphTensor *absoluteTensor = [mpsGraph absoluteWithTensor:inputTensor
                                                                        name:nil];
               outputTensor = [mpsGraph reductionMinimumWithTensor:absoluteTensor
-                                                             axes:axes
+                                                             axes:wrappedAxes
                                                              name:nil];
           } else {
               MPSGraphTensor *absoluteTensor = [mpsGraph absoluteWithTensor:inputTensor
                                                                        name:nil];
 
               MPSGraphTensor *powerValTensor = [mpsGraph constantWithScalar:p
-                                                                   dataType:getMPSDataType(input_t.scalar_type())];
+                                                                   dataType:mps_input_dtype];
 
               MPSGraphTensor *reciprocalPowerValTensor = [mpsGraph constantWithScalar:reciprocal_p
-                                                                             dataType:getMPSDataType(input_t.scalar_type())];
+                                                                             dataType:mps_input_dtype];
 
               MPSGraphTensor *powerTensor = [mpsGraph powerWithPrimaryTensor:absoluteTensor
                                                              secondaryTensor:powerValTensor
                                                                         name:nil];
 
               MPSGraphTensor *reductionSumTensor = [mpsGraph reductionSumWithTensor:powerTensor
-                                                                         axes:axes
+                                                                         axes:wrappedAxes
                                                                          name:nil];
 
               outputTensor = [mpsGraph powerWithPrimaryTensor:reductionSumTensor
                                               secondaryTensor:reciprocalPowerValTensor
                                                          name:nil];
           }
 
-          newCachedGraph->inputTensor_ = inputTensor;
+          if (cdist) {
+            outputTensor= [mpsGraph reshapeTensor:outputTensor withShape:mps::getMPSShape(output_t) name: nil];
+          }
+
           newCachedGraph->outputTensor_ = outputTensor;
         }
         return newCachedGraph;
       });
     }
 
-    auto inputPlaceholder = Placeholder();
-
-    if (apparent_input_shape) {
-      inputPlaceholder = Placeholder(cachedGraph->inputTensor_, input_t, apparent_input_shape);
-    } else {
-      inputPlaceholder = Placeholder(cachedGraph->inputTensor_, input_t);
-    }
-
+    auto otherPlaceholder = Placeholder();
+    auto inputPlaceholder = Placeholder(cachedGraph->inputTensor_, input_t);
     auto outputPlaceholder = Placeholder(cachedGraph->outputTensor_, output_t, apparent_output_shape);
 
+    NSMutableDictionary<MPSGraphTensor*, MPSGraphTensorData*>* feeds =[NSMutableDictionary dictionary];
+    feeds[inputPlaceholder.getMPSGraphTensor()]   = inputPlaceholder.getMPSGraphTensorData();
 
-    NSDictionary<MPSGraphTensor *, MPSGraphTensorData *> *feeds = @{
-      inputPlaceholder.getMPSGraphTensor() : inputPlaceholder.getMPSGraphTensorData(),
-    };
+    if (cdist) {
+      otherPlaceholder = Placeholder(cachedGraph->otherTensor_, other_tensor);
+      feeds[otherPlaceholder.getMPSGraphTensor()] = otherPlaceholder.getMPSGraphTensorData();
+    }
 
     NSDictionary<MPSGraphTensor *, MPSGraphTensorData *> *results = @{
       outputPlaceholder.getMPSGraphTensor() : outputPlaceholder.getMPSGraphTensorData()
     };
 
     runMPSGraph(stream, cachedGraph->graph(), feeds, results);
-
   }
 }
 
+TORCH_IMPL_FUNC(norm_out_mps)
+(const Tensor& self,
+ const OptionalScalarRef opt_p,
+ IntArrayRef dim,
+ bool keepdim,
+ const Tensor& result) {
+  impl_func_norm_mps(self, self, opt_p, dim, keepdim, c10::nullopt, result, /*cdist=*/false);
+}
+
+TORCH_IMPL_FUNC(norm_dtype_out_mps)
+(const Tensor& self,
+ const OptionalScalarRef opt_p,
+ IntArrayRef dim,
+ bool keepdim,
+ ScalarType dtype,
+ const Tensor& result) {
+  impl_func_norm_mps(self, self, opt_p, dim, keepdim, dtype, result, /*cdist=*/false);
+}
+
+
+Tensor _cdist_forward_mps(const Tensor& x1, const Tensor& x2, const double p, c10::optional<int64_t> compute_mode) {
+  using namespace mps;
+  TORCH_CHECK(x1.dim() >= 2, "cdist only supports at least 2D tensors, X1 got: ", x1.dim(), "D");
+  TORCH_CHECK(x2.dim() >= 2, "cdist only supports at least 2D tensors, X2 got: ", x2.dim(), "D");
+  TORCH_CHECK(x1.size(-1) == x2.size(-1), "X1 and X2 must have the same number of columns. X1: ", x1.size(-1), " X2: ", x2.size(-1));
+  TORCH_CHECK(at::isFloatingType(x1.scalar_type()), "cdist only supports floating-point dtypes, X1 got: ", x1.scalar_type());
+  auto device1 = x1.device().type();
+  TORCH_CHECK(at::isFloatingType(x2.scalar_type()), "cdist only supports floating-point dtypes, X2 got: ", x2.scalar_type());
+  auto device2 = x2.device().type();
+  TORCH_CHECK(p >= 0, "cdist only supports non-negative p values");
+  TORCH_CHECK(device1 == device2, "X1 and X2 must have the same device type. X1: ", device1, " X2: ", device2);
+  TORCH_CHECK(x1.is_mps() && (x1.get_device() == x2.get_device()), "device of X1 (", x1.get_device(), ") must match device of X2 (", x2.get_device(), ")");
+
+  int64_t c1 = x1.size(-1);
+  int64_t c2 = x2.size(-1);
+
+  auto dim1 = x1.dim();
+  auto dim2 = x2.dim();
+  int64_t mode = compute_mode.value_or(0);
+  TORCH_CHECK(mode >= 0 && mode <= 2, "possible modes: 0, 1, 2, but was: ", mode);
+
+  int64_t r1 = x1.size(-2);
+  int64_t r2 = x2.size(-2);
+
+  //For batch calculation we expand all dimensions(except the last two) to one, with size that equals to product of them.
+  //The last two dimensions will stay the same
+  IntArrayRef batch_tensor1(x1.sizes().data(), dim1 - 2);
+  IntArrayRef batch_tensor2(x2.sizes().data(), dim2 - 2);
+  std::vector<int64_t> expand_batch_portion = infer_size(batch_tensor1, batch_tensor2);
+  std::vector<int64_t> tensor1_expand_size(expand_batch_portion);
+  tensor1_expand_size.insert(tensor1_expand_size.end(), {r1, c1});
+  std::vector<int64_t> tensor2_expand_size(expand_batch_portion);
+  tensor2_expand_size.insert(tensor2_expand_size.end(), {r2, c2});
+
+  const int64_t expand_batch_product = c10::multiply_integers(expand_batch_portion);
+  std::vector<int64_t> tensor1_view{expand_batch_product, r1, c1};
+  std::vector<int64_t> tensor2_view{expand_batch_product, r2, c2};
+
+  std::vector<int64_t> output_shape(expand_batch_portion);
+  output_shape.insert(output_shape.end(), {r1, r2});
+  Tensor result = at::empty(output_shape, x1.options());
+
+  NormOpBlock norm_op_block = ^NormOpFn(cachedGraph, x1Tensor, x2Tensor) {
+    MPSGraph* mpsGraph = cachedGraph->graph();
+
+    MPSGraphTensor* inputBroadcast = [mpsGraph broadcastTensor:x1Tensor toShape:getMPSShape(tensor1_expand_size) name:nil];
+    MPSGraphTensor* inputBroadcastReshape = [mpsGraph reshapeTensor:inputBroadcast withShape:getMPSShape(tensor1_view) name:nil];
+
+    MPSGraphTensor* otherBroadcast = [mpsGraph broadcastTensor:x2Tensor toShape:getMPSShape(tensor2_expand_size) name:nil];
+    MPSGraphTensor* otherBroadcastReshape = [mpsGraph reshapeTensor:otherBroadcast withShape:getMPSShape(tensor2_view) name:nil];
+
+    NSMutableArray<MPSGraphTensor*> *inputArray = [NSMutableArray arrayWithCapacity:tensor1_view[1]];
+    NSMutableArray<MPSGraphTensor*> *otherArray = [NSMutableArray arrayWithCapacity:tensor2_view[1]];
+
+    for (const auto i : c10::irange(tensor2_view[1])) {
+      inputArray[i] = inputBroadcastReshape;
+    }
+
+    for (const auto i : c10::irange(tensor1_view[1])) {
+      otherArray[i] = otherBroadcastReshape;
+    }
+
+    MPSGraphTensor *inputTensorReshaped = [mpsGraph concatTensors:inputArray dimension:1 interleave:YES name:nil];
+    MPSGraphTensor *otherTensorReshaped = [mpsGraph concatTensors:otherArray dimension:1 interleave:NO name:nil];
+
+
+    MPSGraphTensor *inputTensorPNorm = [mpsGraph subtractionWithPrimaryTensor: inputTensorReshaped
+                                                              secondaryTensor: otherTensorReshaped
+                                                                         name: nil];
+    return inputTensorPNorm;
+  };
+
+  c10::optional<IntArrayRef> inputBroadcastSize = c10::make_optional(makeArrayRef(tensor1_view.data(), tensor1_view.size()));
+  impl_func_norm_mps(x1, x2, OptionalScalarRef(p), makeArrayRef<int64_t>(2), false, c10::nullopt, result, /*cdist=*/true, inputBroadcastSize, norm_op_block);
+  return result;
+}
+
 Tensor std_var_common_impl_mps(const Tensor & input_t,
                                at::OptionalIntArrayRef dim,
                                c10::optional<int64_t> correction,

diff --git a/aten/src/ATen/native/native_functions.yaml b/aten/src/ATen/native/native_functions.yaml
@@ -4090,6 +4090,7 @@
 - func: _cdist_forward(Tensor x1, Tensor x2, float p, int? compute_mode) -> Tensor
   dispatch:
     CPU, CUDA: _cdist_forward
+    MPS: _cdist_forward_mps
   autogen: _cdist_forward.out
 
 - func: _cdist_backward(Tensor grad, Tensor x1, Tensor x2, float p, Tensor cdist) -> Tensor
@@ -6174,6 +6175,7 @@
   device_check: NoCheck   # TensorIterator
   dispatch:
     CPU, CUDA: norm_dtype_out
+    MPS: norm_dtype_out_mps
 
 - func: norm.out(Tensor self, Scalar? p, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
   structured: True