!14498 [GraphKernel]remove redundant cast bias for matmul

From: @lingyunli63
Reviewed-by: @gaoxiong1,@ckey_dou
Signed-off-by: @ckey_dou

mindspore/ccsrc/backend/optimizer/graph_kernel/graph_kernel_optimization.cc

@@ -30,6 +30,7 @@
 #include "backend/optimizer/graph_kernel/tensor_promotion.h"
 #include "backend/optimizer/graph_kernel/graph_kernel_splitter.h"
 #include "backend/optimizer/graph_kernel/graph_kernel_expander.h"
+#include "backend/optimizer/graph_kernel/optimize_matmul.h"
 #include "backend/optimizer/graph_kernel/raise_reduction_precision.h"
 #include "backend/optimizer/graph_kernel/graph_kernel_cse.h"
 #include "backend/optimizer/graph_kernel/shape_ops_splitter.h"
@@ -49,8 +50,11 @@ PassManagerPtr GraphKernelOptimizer::PreProcess() {
   // Change Assign(p, a, U) to Assign(Depend(p, U), a)
   pm->AddPass(std::make_shared<SplitAssign>());
 
-  // Reorder TransData-Cast to Cast-TransData,
   if (is_ascend) {
+    // Remove redundant Cast(bias, fp16) for Matmul input
+    pm->AddPass(std::make_shared<OptimizeMatmul>());
+
+    // Reorder TransData-Cast to Cast-TransData
     pm->AddPass(std::make_shared<ReorderOps>());
   }
 
@@ -81,7 +85,7 @@ PassManagerPtr GraphKernelOptimizer::HighLevelOpt1() {
   pm->AddPass(std::make_shared<OptimizeAssign>());
   pm->AddPass(std::make_shared<EliminateRedundantOutput>());
 
-  // Cast the input of ReduceSum from float16 to float32 for higher precision*/
+  // Cast the input of ReduceSum from float16 to float32 for higher precision
   pm->AddPass(std::make_shared<RaiseReductionPrecision>());
 
   // Universal arithmetic simplify

mindspore/ccsrc/backend/optimizer/graph_kernel/optimize_matmul.cc

@@ -0,0 +1,64 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "backend/optimizer/graph_kernel/optimize_matmul.h"
+#include <tuple>
+#include "backend/session/anf_runtime_algorithm.h"
+#include "backend/kernel_compiler/common_utils.h"
+#include "backend/optimizer/graph_kernel/graph_kernel_helper.h"
+
+namespace mindspore {
+namespace opt {
+/* MatMul supports fp32 bias, so remove the redundant cast when cast only used by MatMul
+ *
+ *   %0 = cast(bias_fp32, fp16)
+ *   %1 = MatMul(A_fp16, B_fp16, %0)
+ *   ------>
+ *   %1 = MatMul(A_fp16, B_fp16, bias_fp32)
+ */
+bool OptimizeMatmul::Run(const FuncGraphPtr &func_graph) {
+  MS_EXCEPTION_IF_NULL(func_graph);
+  auto mng = func_graph->manager();
+  if (mng == nullptr) {
+    mng = Manage(func_graph, true);
+    func_graph->set_manager(mng);
+  }
+  auto changed = false;
+  auto nodes = TopoSort(func_graph->get_return());
+  for (auto node : nodes) {
+    if (!IsPrimitiveCNode(node, prim::kPrimMatMul)) {
+      continue;
+    }
+    auto cnode = node->cast<CNodePtr>();
+    if (cnode->size() != 4) {
+      continue;
+    }
+    auto cast_node = cnode->input(3);
+    if (!IsPrimitiveCNode(cast_node, prim::kPrimCast)) {
+      continue;
+    }
+    auto cast_input_type = AnfAlgo::GetInputDeviceDataType(cast_node, 0);
+    auto cast_output_type = AnfAlgo::GetOutputDeviceDataType(cast_node, 0);
+    if (cast_input_type == kNumberTypeFloat32 && cast_output_type == kNumberTypeFloat16 &&
+        mng->node_users()[cast_node].size() == 1) {
+      mng->Replace(cast_node, (cast_node->cast<CNodePtr>())->input(1));
+      changed = true;
+    }
+  }
+
+  return changed;
+}
+}  // namespace opt
+}  // namespace mindspore

mindspore/ccsrc/backend/optimizer/graph_kernel/optimize_matmul.h

@@ -0,0 +1,36 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef MINDSPORE_CCSRC_BACKEND_OPTIMIZER_GRAPH_KERNEL_OPTIMIZE_MATMUL_H_
+#define MINDSPORE_CCSRC_BACKEND_OPTIMIZER_GRAPH_KERNEL_OPTIMIZE_MATMUL_H_
+
+#include <map>
+#include <memory>
+
+#include "backend/optimizer/common/pass.h"
+#include "ir/func_graph.h"
+
+namespace mindspore {
+namespace opt {
+class OptimizeMatmul : public Pass {
+ public:
+  OptimizeMatmul() : Pass("optimize_matmul") {}
+  ~OptimizeMatmul() override = default;
+  bool Run(const FuncGraphPtr &graph) override;
+};
+using OptimizeMatmulPtr = std::shared_ptr<OptimizeMatmul>;
+}  // namespace opt
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_BACKEND_OPTIMIZER_GRAPH_KERNEL_OPTIMIZE_MATMUL_H_

tests/st/ops/graph_kernel/test_matmul_cast.py

@@ -0,0 +1,60 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.nn import Cell
+import mindspore.ops.operations as P
+from mindspore.common import dtype as mstype
+
+class Net(Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.matmul = P.MatMul(transpose_a=True, transpose_b=True)
+        self.add = P.BiasAdd()
+        self.cast = P.Cast()
+
+    def construct(self, x, y, b):
+        xy = self.matmul(x, y)
+        b16 = self.cast(b, mstype.float16)
+        res = self.add(xy, b16)
+        return self.cast(res, mstype.float32)
+
+def get_output(i0, i1, i2, enable_graph_kernel=False):
+    if enable_graph_kernel:
+        context.set_context(enable_graph_kernel=True, save_graphs=False)
+    net = Net()
+    output = net(i0, i1, i2)
+    return output
+
+def test_basic():
+    i0 = Tensor(np.random.normal(1, 0.01, [800, 96]).astype(np.float16))
+    i1 = Tensor(np.random.normal(1, 0.01, [128, 800]).astype(np.float16))
+    i2 = Tensor(np.random.normal(100, 0.1, [128,]).astype(np.float32))
+    expect = get_output(i0, i1, i2, False)
+    output = get_output(i0, i1, i2, True)
+    expect_np = expect.asnumpy().copy()
+    output_np = output.asnumpy().copy()
+    assert np.allclose(expect_np, output_np, 1.e-4, 1.e-7)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_basic_ascend():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    test_basic()