[PIR]Migrate API fused_linear_activation,fused_matmul_bias,softmax_mask_fuse,FusedLinear to PIR

paddle/fluid/pir/dialect/operator/ir/manual_api.cc

@@ -225,6 +225,26 @@ pir::Value assign(const pir::Value& x) {
   }
 }
 
+std::tuple<pir::Value, pir::Value> fused_gemm_epilogue(pir::Value x,
+                                                       pir::Value y,
+                                                       pir::Value bias,
+                                                       bool trans_x,
+                                                       bool trans_y,
+                                                       std::string activation) {
+  pir::IrContext* ctx = pir::IrContext::Instance();
+  pir::AttributeMap attribute_map = {
+      {"trans_x", pir::BoolAttribute::get(ctx, trans_x)},
+      {"trans_y", pir::BoolAttribute::get(ctx, trans_y)},
+      {"activation", pir::StrAttribute::get(ctx, activation)}};
+  auto fused_gemm_epilogue_op =
+      ApiBuilder::Instance()
+          .GetBuilder()
+          ->Build<paddle::dialect::FusedGemmEpilogueOp>(
+              x, y, bias, attribute_map);
+  return std::make_tuple(fused_gemm_epilogue_op.result(0),
+                         fused_gemm_epilogue_op.result(1));
+}
+
 pir::Value array_pop(pir::Value input, int index) {
   if (input.type().isa<paddle::dialect::DenseTensorArrayType>()) {
     paddle::dialect::ArrayPopOp array_pop_op =

paddle/fluid/pir/dialect/operator/ir/manual_api.h

@@ -87,6 +87,12 @@ pir::Value slice_array_dense(pir::Value input, pir::Value starts);
 
 pir::Value assign(const pir::Value& x);
 
+std::tuple<pir::Value, pir::Value> fused_gemm_epilogue(pir::Value x,
+                                                       pir::Value y,
+                                                       pir::Value bias,
+                                                       bool trans_x,
+                                                       bool trans_y,
+                                                       std::string activation);
 pir::Value array_pop(pir::Value input, int index);
 
 }  // namespace dialect

paddle/fluid/pir/dialect/operator/ir/ops.yaml

@@ -661,6 +661,16 @@
   view : (mean -> mean_out), (variance -> variance_out)
   backward : fused_bn_add_activation_grad
 
+- op : fused_softmax_mask
+  args : (Tensor x, Tensor mask)
+  output : Tensor(out)
+  infer_meta :
+    func : SoftmaxMaskFuseInferMeta
+  kernel :
+    func : fused_softmax_mask
+    data_type : x
+  backward: fused_softmax_mask_grad
+
 - op : fused_softmax_mask_upper_triangle
   args : (Tensor X)
   output : Tensor(Out)

paddle/fluid/pir/dialect/operator/ir/ops_backward.yaml

@@ -324,6 +324,17 @@
     func: fused_feedforward_grad
   optional: linear1_bias, linear2_bias, ln1_scale, ln1_bias, ln1_out, ln1_mean, ln1_variance, ln2_scale, ln2_bias, ln2_mean, ln2_variance, dropout2_out, ln1_scale_grad, ln1_bias_grad, ln2_scale_grad, ln2_bias_grad,  linear2_bias_grad
 
+- backward_op : fused_softmax_mask_grad
+  forward : fused_softmax_mask (Tensor x, Tensor mask) -> Tensor(out)
+  args : (Tensor out, Tensor out_grad)
+  output : Tensor(x_grad)
+  infer_meta :
+    func : GeneralUnaryGradInferMeta
+    param: [out]
+  kernel :
+    func : fused_softmax_mask_grad
+    data_type : out
+
 - backward_op : fused_softmax_mask_upper_triangle_grad
   forward : fused_softmax_mask_upper_triangle(Tensor X) -> Tensor(Out)
   args: (Tensor Out, Tensor Out_grad)

paddle/fluid/pybind/manual_static_op_function.h

@@ -754,6 +754,39 @@ static PyObject *run_custom_op(PyObject *self,
   }
 }
 
+static PyObject *static_api_fused_gemm_epilogue(PyObject *self,
+                                                PyObject *args,
+                                                PyObject *kwargs) {
+  try {
+    VLOG(6) << "Running Static API: fused_gemm_epilogue";
+
+    VLOG(8) << "args count: " << (PyTuple_Size(args) / 2);
+    // Get OpResult from args
+    PyObject *x_obj = PyTuple_GET_ITEM(args, 0);
+    auto x = CastPyArg2Value(x_obj, "fused_gemm_epilogue", 0);
+    PyObject *y_obj = PyTuple_GET_ITEM(args, 1);
+    auto y = CastPyArg2Value(y_obj, "fused_gemm_epilogue", 1);
+    PyObject *bias_obj = PyTuple_GET_ITEM(args, 2);
+    auto bias = CastPyArg2Value(bias_obj, "fused_gemm_epilogue", 2);
+
+    // Parse Attributes if needed
+    PyObject *trans_x_obj = PyTuple_GET_ITEM(args, 3);
+    bool trans_x = CastPyArg2Boolean(trans_x_obj, "fused_gemm_epilogue", 3);
+    PyObject *trans_y_obj = PyTuple_GET_ITEM(args, 4);
+    bool trans_y = CastPyArg2Boolean(trans_y_obj, "fused_gemm_epilogue", 4);
+    PyObject *activation_obj = PyTuple_GET_ITEM(args, 5);
+    std::string activation =
+        CastPyArg2String(activation_obj, "fused_gemm_epilogue", 5);
+
+    // Call ir static api
+    auto out = paddle::dialect::fused_gemm_epilogue(
+        x, y, bias, trans_x, trans_y, activation);
+    return ToPyObject(out);
+  } catch (...) {
+    ThrowExceptionToPython(std::current_exception());
+    return nullptr;
+  }
+}
 static PyObject *static_api_array_pop(PyObject *self,
                                       PyObject *args,
                                       PyObject *kwargs) {
@@ -778,6 +811,22 @@ static PyObject *static_api_array_pop(PyObject *self,
   }
 }
 
+extern PyObject *eager_api_fused_gemm_epilogue(PyObject *self,
+                                               PyObject *args,
+                                               PyObject *kwargs);
+
+static PyObject *fused_gemm_epilogue(PyObject *self,
+                                     PyObject *args,
+                                     PyObject *kwargs) {
+  if (egr::Controller::Instance().GetCurrentTracer() == nullptr) {
+    VLOG(6) << "Call static_api_fused_gemm_epilogue";
+    return static_api_fused_gemm_epilogue(self, args, kwargs);
+  } else {
+    VLOG(6) << "Call eager_api_fused_gemm_epilogue";
+    return eager_api_fused_gemm_epilogue(self, args, kwargs);
+  }
+}
+
 static PyMethodDef ManualOpsAPI[] = {
     {"set_parameter",
      (PyCFunction)(void (*)(void))static_api_set_parameter,
@@ -823,6 +872,10 @@ static PyMethodDef ManualOpsAPI[] = {
      (PyCFunction)(void (*)(void))static_api_slice_array_dense,
      METH_VARARGS | METH_KEYWORDS,
      "C++ interface function for slice_array_dense."},
+    {"fused_gemm_epilogue",
+     (PyCFunction)(void (*)(void))fused_gemm_epilogue,
+     METH_VARARGS | METH_KEYWORDS,
+     "C++ interface function for fused_gemm_epilogue."},
     {"_run_custom_op",
      (PyCFunction)(void (*)(void))run_custom_op,
      METH_VARARGS | METH_KEYWORDS,

paddle/phi/api/lib/api_custom_impl.cc

@@ -25,13 +25,18 @@ limitations under the License. */
 #include "paddle/phi/core/meta_tensor.h"
 #include "paddle/phi/infermeta/backward.h"
 #include "paddle/phi/infermeta/binary.h"
+#include "paddle/phi/infermeta/fusion.h"
 #include "paddle/phi/infermeta/multiary.h"
 #include "paddle/phi/infermeta/nullary.h"
 #include "paddle/phi/infermeta/unary.h"
+#include "paddle/utils/flags.h"
 #ifdef PADDLE_WITH_DISTRIBUTE
 #include "paddle/phi/core/distributed/auto_parallel/reshard/reshard_utils.h"
 #include "paddle/phi/infermeta/spmd_rules/rules.h"
 #endif
+
+PD_DECLARE_int32(low_precision_op_list);
+
 namespace paddle {
 namespace experimental {
 
@@ -221,6 +226,114 @@ Tensor copy_to_impl(const Tensor& x, Place place, bool blocking) {
   return out;
 }
 
+std::tuple<Tensor, Tensor> fused_gemm_epilogue_impl(
+    const Tensor& x,
+    const Tensor& y,
+    const Tensor& bias,
+    bool trans_x,
+    bool trans_y,
+    const std::string& activation) {
+  Backend kernel_backend = Backend::UNDEFINED;
+  DataLayout kernel_layout = DataLayout::UNDEFINED;
+  DataType kernel_data_type = DataType::UNDEFINED;
+
+  if (kernel_backend == Backend::UNDEFINED ||
+      kernel_layout == DataLayout::UNDEFINED ||
+      kernel_data_type == DataType::UNDEFINED) {
+    auto kernel_key_set = ParseKernelKeyByInputArgs(x, y, bias);
+    auto kernel_key = kernel_key_set.GetHighestPriorityKernelKey();
+    if (kernel_backend == Backend::UNDEFINED) {
+      kernel_backend = kernel_key.backend();
+    }
+    if (kernel_layout == DataLayout::UNDEFINED) {
+      kernel_layout = kernel_key.layout();
+    }
+    if (kernel_data_type == DataType::UNDEFINED) {
+      kernel_data_type = kernel_key.dtype();
+    }
+  }
+
+  VLOG(6) << "fused_gemm_epilogue API kernel key: [" << kernel_backend << ", "
+          << kernel_layout << ", " << kernel_data_type << "]";
+  auto kernel_result = phi::KernelFactory::Instance().SelectKernelOrThrowError(
+      "fused_gemm_epilogue",
+      {kernel_backend, kernel_layout, kernel_data_type},
+      true);
+  const auto& kernel = kernel_result.kernel;
+  if (FLAGS_low_precision_op_list) {
+    phi::KernelFactory::Instance().AddToLowPrecisionKernelList(
+        "fused_gemm_epilogue", kernel_data_type);
+  }
+  VLOG(6) << "fused_gemm_epilogue kernel: " << kernel;
+  // add actual_kernel_backend to select actual kernel backend after a potential
+  // falling-back to CPU
+  Backend actual_kernel_backend =
+      kernel_result.has_fallback_cpu ? Backend::CPU : kernel_backend;
+  auto* dev_ctx = GetDeviceContextByBackend(actual_kernel_backend);
+
+  auto input_x = PrepareData(
+      x,
+      GetKernelInputArgDef(kernel.InputAt(0), actual_kernel_backend),
+      {},
+      kernel_result.is_stride_kernel);
+  auto input_y = PrepareData(
+      y,
+      GetKernelInputArgDef(kernel.InputAt(1), actual_kernel_backend),
+      {},
+      kernel_result.is_stride_kernel);
+  auto input_bias = PrepareData(
+      bias,
+      GetKernelInputArgDef(kernel.InputAt(2), actual_kernel_backend),
+      {},
+      kernel_result.is_stride_kernel);
+
+  std::tuple<Tensor, Tensor> api_output;
+  auto kernel_out_0 = SetKernelOutput(&std::get<0>(api_output));
+  phi::DenseTensor* kernel_out_1 = nullptr;
+  if (activation != "none") {
+    kernel_out_1 = SetKernelOutput(&std::get<1>(api_output));
+  }
+
+  phi::MetaTensor meta_out_0(kernel_out_0, kernel_result.is_stride_kernel);
+  phi::MetaTensor meta_out_1(kernel_out_1, kernel_result.is_stride_kernel);
+
+  phi::FusedGemmEpilogueInferMeta(MakeMetaTensor(*input_x),
+                                  MakeMetaTensor(*input_y),
+                                  MakeMetaTensor(*input_bias),
+                                  trans_x,
+                                  trans_y,
+                                  activation,
+                                  kernel_out_0 ? &meta_out_0 : nullptr,
+                                  kernel_out_1 ? &meta_out_1 : nullptr);
+
+  using kernel_signature = void (*)(const phi::DeviceContext&,
+                                    const phi::DenseTensor&,
+                                    const phi::DenseTensor&,
+                                    const phi::DenseTensor&,
+                                    bool,
+                                    bool,
+                                    const std::string&,
+                                    phi::DenseTensor*,
+                                    phi::DenseTensor*);
+  auto* kernel_fn = kernel.GetVariadicKernelFn<kernel_signature>();
+
+  (*kernel_fn)(*dev_ctx,
+               *input_x,
+               *input_y,
+               *input_bias,
+               trans_x,
+               trans_y,
+               activation,
+               kernel_out_0,
+               kernel_out_1);
+
+  if (kernel_result.has_fallback_cpu) {
+    TransDataBackend(kernel_out_0, kernel_backend, kernel_out_0);
+    TransDataBackend(kernel_out_1, kernel_backend, kernel_out_1);
+  }
+  return api_output;
+}
+
 ////////////////// Backward(grad) api impls //////////////////////
 
 void embedding_grad_impl(const Tensor& x,

paddle/phi/api/lib/api_custom_impl.h

@@ -35,6 +35,14 @@ Tensor add_n_impl(const std::vector<Tensor>& x);
 
 Tensor copy_to_impl(const Tensor& x, Place place, bool blocking);
 
+std::tuple<Tensor, Tensor> fused_gemm_epilogue_impl(
+    const Tensor& x,
+    const Tensor& y,
+    const Tensor& bias,
+    bool trans_x,
+    bool trans_y,
+    const std::string& activation);
+
 ////////////////// Backward(grad) api impls //////////////////////
 
 void imag_grad_impl(const Tensor& out_grad, Tensor* x_grad);

paddle/phi/api/yaml/legacy_backward.yaml

@@ -281,6 +281,17 @@
     data_type : out_grad
   optional : reserve_space
 
+- backward_op : fused_softmax_mask_grad
+  forward : fused_softmax_mask (Tensor x, Tensor mask) -> Tensor(out)
+  args : (Tensor out, Tensor out_grad)
+  output : Tensor(x_grad)
+  infer_meta :
+    func : GeneralUnaryGradInferMeta
+    param: [out]
+  kernel :
+    func : fused_softmax_mask_grad
+    data_type : out
+
 - backward_op : fused_softmax_mask_upper_triangle_grad
   forward : fused_softmax_mask_upper_triangle(Tensor X) -> Tensor(Out)
   args: (Tensor Out, Tensor Out_grad)
@@ -855,6 +866,16 @@
     func: check_model_nan_inf
     data_type: out_grad
 
+- backward_op: fused_gemm_epilogue_grad
+  forward : fused_gemm_epilogue(Tensor x, Tensor y, Tensor bias, bool trans_x, bool trans_y, str activation) -> Tensor(out), Tensor(reserve_space)
+  args : (Tensor x, Tensor y, Tensor reserve_space, Tensor out_grad, bool trans_x, bool trans_y, str activation)
+  output : Tensor(x_grad), Tensor(y_grad), Tensor(bias_grad)
+  infer_meta :
+    func : FusedGemmEpilogueGradInferMeta
+  kernel:
+    func : fused_gemm_epilogue_grad
+  optional : reserve_space
+
 - backward_op: unpool_grad
   forward: unpool (Tensor x, Tensor indices, int[] ksize, int[] strides, int[] padding,  IntArray output_size, str data_format) -> Tensor(out)
   args: (Tensor x, Tensor indices, Tensor out, Tensor out_grad, int[] ksize, int[] strides, int[] padding, IntArray output_size, str data_format)

paddle/phi/api/yaml/legacy_ops.yaml

@@ -584,6 +584,23 @@
   view : (mean -> mean_out), (variance -> variance_out)
   backward : fused_bn_add_activation_grad
 
+- op : fused_gemm_epilogue
+  args : (Tensor x, Tensor y, Tensor bias, bool trans_x, bool trans_y, str activation)
+  output : Tensor(out), Tensor(reserve_space)
+  invoke : fused_gemm_epilogue_impl(x, y, bias, trans_x, trans_y, activation)
+  backward: fused_gemm_epilogue_grad
+  optional: reserve_space
+
+- op : fused_softmax_mask
+  args : (Tensor x, Tensor mask)
+  output : Tensor(out)
+  infer_meta :
+    func : SoftmaxMaskFuseInferMeta
+  kernel :
+    func : fused_softmax_mask
+    data_type : x
+  backward: fused_softmax_mask_grad
+
 - op : fused_softmax_mask_upper_triangle
   args : (Tensor X)
   output : Tensor(Out)

python/paddle/distributed/fleet/layers/mpu/mp_layers.py

@@ -16,7 +16,6 @@
 
 import paddle
 from paddle.autograd import PyLayer
-from paddle.base import core
 from paddle.distributed import fleet
 from paddle.nn import functional as F
 
@@ -34,7 +33,7 @@
 
 
 def is_fused_matmul_bias_supported():
-    return hasattr(core.eager.ops.legacy, 'fused_gemm_epilogue')
+    return hasattr(paddle._C_ops, 'fused_gemm_epilogue')
 
 
 def is_fused_linear_param_grad_add_supported():
@@ -214,7 +213,10 @@ def forward(
         if not fuse_matmul_bias:
             return paddle._C_ops.linear(x, weight, bias)
         else:
-            return paddle._legacy_C_ops.fused_gemm_epilogue(x, weight, bias)
+            result, _ = paddle._C_ops.fused_gemm_epilogue(
+                x, weight, bias, False, False, "none"
+            )
+            return result
 
     @staticmethod
     def backward(ctx, dy):