[PIR & Inference] Refactor fused_weight_only_linear_pass

paddle/fluid/pir/drr/api/tensor_interface.cc

@@ -30,5 +30,7 @@ bool DtypeInterface::operator==(const DtypeInterface& other) const {
   return *dtype_ == *other.dtype_;
 }
 
+IrDtype DtypeInterface::dtype() const { return *(this->dtype_); }
+
 }  // namespace drr
 }  // namespace pir

paddle/fluid/pir/drr/api/tensor_interface.h

@@ -42,6 +42,7 @@ class ShapeInterface final {
 class DtypeInterface final {
  public:
   bool operator==(const DtypeInterface& other) const;
+  IrDtype dtype() const;
 
  private:
   explicit DtypeInterface(const IrDtype* dtype) : dtype_(dtype) {}

paddle/fluid/pir/drr/ir_value.h

@@ -44,6 +44,11 @@ class IrDtype {
 
   bool operator==(IrDtype other) const { return dtype_ == other.dtype_; }
 
+  template <typename T>
+  bool isa() const {
+    return dtype_.isa<T>();
+  }
+
  private:
   const pir::Type dtype_;
 };

paddle/fluid/pir/transforms/fusion/fused_weight_only_linear_pass.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/pir/transforms/fusion/fused_weight_only_linear_pass.h"
+#include "paddle/fluid/pir/dialect/operator/ir/pd_op.h"
 #include "paddle/fluid/pir/drr/api/drr_pattern_base.h"
 #include "paddle/fluid/platform/device/gpu/gpu_info.h"
 #include "paddle/fluid/platform/place.h"
@@ -22,11 +23,14 @@
 
 namespace {
 
-inline int getSMVersion() {
+int getSMVersion() {
   int sm_version = 80;
 #if defined(PADDLE_WITH_CUDA)
   sm_version = paddle::platform::GetGPUComputeCapability(
       paddle::platform::GetCurrentDeviceId());
+#else
+  PADDLE_THROW(paddle::platform::errors::Unavailable(
+      "fused_weight_only_linear_pass needs paddle compiled with CUDA."));
 #endif
   return sm_version;
 }
@@ -40,12 +44,14 @@ class FusedWeightOnlyLinearPattern
     //
     pir::drr::SourcePattern src = ctx->SourcePattern();
     const auto &matmul =
-        src.Op("pd_op.matmul",
+        src.Op(paddle::dialect::MatmulOp::name(),
                {{"transpose_x", src.Attr("matmul_transpose_x")},
                 {"transpose_y", src.Attr("matmul_transpose_y")}});
+    const auto &parameter = src.Op(
+        pir::ParameterOp::name(), {{"parameter_name", src.Attr("param_name")}});
+    src.Tensor("w") = parameter();
     src.Tensor("matmul_out") = matmul(src.Tensor("x"), src.Tensor("w"));
-
-    const auto &add = src.Op("pd_op.add");
+    const auto &add = src.Op(paddle::dialect::AddOp::name());
     src.Tensor("add_out") = add(src.Tensor("matmul_out"), src.Tensor("bias"));
 
     //
@@ -62,6 +68,17 @@ class FusedWeightOnlyLinearPattern
         return false;
       }
 
+      auto w_dims = match_ctx.Tensor("w").Shape();
+      if (w_dims.at(0) % 64 != 0 || w_dims.at(1) % 16 != 0) return false;
+
+      auto w_dtype = match_ctx.Tensor("w").Dtype();
+      if (!w_dtype.dtype().isa<pir::Float16Type>() &&
+          !w_dtype.dtype().isa<pir::BFloat16Type>())
+        return false;
+
+      auto x_dims = match_ctx.Tensor("x").Shape();
+      if (x_dims.at(x_dims.size() - 1) != w_dims.at(1)) return false;
+
       return true;
     });
     //
@@ -74,15 +91,15 @@ class FusedWeightOnlyLinearPattern
         res.Attr([](const pir::drr::MatchContext &match_ctx) -> std::any {
           return "weight_only_int8";
         });
-    // int arch = getSMVersion();
-    const auto &weight_quantize_arch_attr =
-        res.Attr([&](const pir::drr::MatchContext &match_ctx) -> std::any {
-          return 80;
+
+    const auto &arch_attr =
+        res.Attr([&](const pir::drr::MatchContext &match_ctx) -> int {
+          return getSMVersion();
         });
 
-    const auto &weight_quantize = res.Op(
-        "pd_op.weight_quantize",
-        {{"algo", weight_only_int8_attr}, {"arch", weight_quantize_arch_attr}});
+    const auto &weight_quantize =
+        res.Op(paddle::dialect::WeightQuantizeOp::name(),
+               {{"algo", weight_only_int8_attr}, {"arch", arch_attr}});
     weight_quantize({&res.Tensor("w")},
                     {&res.Tensor("quanted_weight_tensor"),
                      &res.Tensor("weight_scale_tensor")});
@@ -92,12 +109,9 @@ class FusedWeightOnlyLinearPattern
           return "int8";
         });
 
-    const auto &weight_only_linear_arch_attr = res.Attr(
-        [&](const pir::drr::MatchContext &match_ctx) -> int { return 80; });
     const auto &weight_only_linear =
-        res.Op("pd_op.weight_only_linear",
-               {{"weight_dtype", weight_dtype_attr},
-                {"arch", weight_only_linear_arch_attr}});
+        res.Op(paddle::dialect::WeightOnlyLinearOp::name(),
+               {{"weight_dtype", weight_dtype_attr}, {"arch", arch_attr}});
     weight_only_linear({&res.Tensor("x"),
                         &res.Tensor("quanted_weight_tensor"),
                         &res.Tensor("bias"),
@@ -119,8 +133,8 @@ class FusedWeightOnlyLinearPass : public pir::PatternRewritePass {
 
   bool CanApplyOn(pir::Operation *op) const override {
     int sm_vesion = getSMVersion();
-    if (sm_vesion != 70 && sm_vesion != 80 && sm_vesion != 86 &&
-        sm_vesion != 75) {
+    if (sm_vesion != 70 && sm_vesion != 75 && sm_vesion != 80 &&
+        sm_vesion != 86) {
       return false;
     }
     return op->num_regions() > 0;

test/ir/pir/fused_pass/test_fused_weight_only_linear_pass.py

@@ -19,6 +19,7 @@
 
 import paddle
 from paddle.base import core
+from paddle.pir.core import create_parameter
 
 np.random.seed(2013)
 
@@ -56,34 +57,38 @@ def build_ir_progam(self):
                 x = paddle.static.data(
                     name='x', shape=[3, 64, 64], dtype=self.dtype
                 )
-                w = paddle.static.data(
-                    name="w", shape=[64, 64], dtype=self.dtype
+                initializer = paddle.nn.initializer.Constant(0.0)
+                w = create_parameter(
+                    shape=[64, 64], dtype=self.dtype, initializer=initializer
                 )
                 bias_ = paddle.static.data(
-                    name="bias", shape=[64], dtype=self.dtype
+                    name="bias",
+                    shape=[64],
+                    dtype=self.dtype,
                 )
                 bias = paddle.assign(bias_)
                 res1 = paddle.matmul(x=x, y=w)
                 out = paddle.add(res1, bias)
-
         self.pass_list = ['fused_weight_only_linear_pass']
         self.feeds = {
             "x": np.random.random((3, 64, 64)).astype(self.dtype),
             "w": np.random.random((64, 64)).astype(self.dtype),
             "bias": np.random.random(64).astype(self.dtype),
         }
         self.fetch_list = [out]
-        self.valid_op_map = {
-            "pd_op.weight_only_linear": 1,
-            "pd_op.weight_quantize": 1,
-            "pd_op.matmul": 0,
-            "pd_op.add": 0,
-        }
+
         return pir_program
 
     def setUp(self):
         self.place_runtime = "gpu"
         self.dtype = 'float32'
+        # weight_quantize need weight's dtype to be fp16 or bf16
+        self.valid_op_map = {
+            "pd_op.weight_only_linear": 0,
+            "pd_op.weight_quantize": 0,
+            "pd_op.matmul": 1,
+            "pd_op.add": 1,
+        }
 
     def sample_program(self):
         yield self.build_ir_progam(), False
@@ -96,6 +101,56 @@ class TestFusedWeightOnlyLinearPass_Fp16(TestFusedWeightOnlyLinearPass_Fp32):
     def setUp(self):
         self.place_runtime = "gpu"
         self.dtype = 'float16'
+        self.valid_op_map = {
+            "pd_op.weight_only_linear": 1,
+            "pd_op.weight_quantize": 1,
+            "pd_op.matmul": 0,
+            "pd_op.add": 0,
+        }
+
+
+class TestFusedWeightOnlyLinearPass_wdim_divisible_by_16(
+    TestFusedWeightOnlyLinearPass_Fp32
+):
+    def build_ir_progam(self):
+        pir_program = None
+        with paddle.pir_utils.IrGuard():
+            pir_program = paddle.static.Program()
+            with paddle.pir.core.program_guard(pir_program):
+                x = paddle.static.data(
+                    name='x', shape=[3, 64, 64], dtype=self.dtype
+                )
+                initializer = paddle.nn.initializer.Constant(0.0)
+                w = create_parameter(
+                    shape=[64, 15], dtype=self.dtype, initializer=initializer
+                )
+                bias_ = paddle.static.data(
+                    name="bias",
+                    shape=[15],
+                    dtype=self.dtype,
+                )
+                bias = paddle.assign(bias_)
+                res1 = paddle.matmul(x=x, y=w)
+                out = paddle.add(res1, bias)
+        self.pass_list = ['fused_weight_only_linear_pass']
+        self.feeds = {
+            "x": np.random.random((3, 64, 64)).astype(self.dtype),
+            "w": np.random.random((64, 15)).astype(self.dtype),
+            "bias": np.random.random(15).astype(self.dtype),
+        }
+        self.fetch_list = [out]
+
+        return pir_program
+
+    def setUp(self):
+        self.place_runtime = "gpu"
+        self.dtype = 'float16'
+        self.valid_op_map = {
+            "pd_op.weight_only_linear": 0,
+            "pd_op.weight_quantize": 0,
+            "pd_op.matmul": 1,
+            "pd_op.add": 1,
+        }
 
 
 if __name__ == "__main__":