!11785 gpu RangeOp better error handling in kernel

From: @peilin-wang
Reviewed-by: @robingrosman,@tom__chen
Signed-off-by: @tom__chen

mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/dynamic_range_gpu_kernel.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2021 Huawei Technologies Co., Ltd
+ * Copyright 2020-2021 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.
@@ -44,22 +44,58 @@ class DynamicRangeGpuKernel : public GpuKernel {
     T *range_delta = GetDeviceAddress<T>(inputs, 2);
     T *output_device_address = GetDeviceAddress<T>(outputs, 0);
     int64_t *output_shape_device_address = GetDeviceAddress<int64_t>(workspace, 0);
+    DynamicRangeErrorCode *error_code_device_address = GetDeviceAddress<DynamicRangeErrorCode>(workspace, 1);
 
     stream_ptr_ = stream_ptr;
 
-    CalRange(range_start, range_end, range_delta, output_device_address, output_shape_device_address,
-             max_output_length_, reinterpret_cast<cudaStream_t>(stream_ptr));
+    CudaValidateInputAndInferShape(range_start, range_end, range_delta, output_shape_device_address,
+                                   error_code_device_address, max_output_length_,
+                                   reinterpret_cast<cudaStream_t>(stream_ptr));
+
+    DynamicRangeErrorCode error_code = DynamicRangeErrorCode::kOk;
+
+    CHECK_CUDA_RET_WITH_ERROR(c_node_ptr_,
+                              cudaMemcpyAsync(&error_code, error_code_device_address, sizeof(DynamicRangeErrorCode),
+                                              cudaMemcpyDeviceToHost, reinterpret_cast<cudaStream_t>(stream_ptr)),
+                              "Failed to copy error code to host.");
+    CHECK_CUDA_RET_WITH_EXCEPT(c_node_ptr_, cudaDeviceSynchronize(), "cudaDeviceSyncFailed");
 
     // use workspace[0] for actual output shape, we know it must be 1d
     CHECK_CUDA_RET_WITH_ERROR(c_node_ptr_,
                               cudaMemcpyAsync(&output_shape_, output_shape_device_address, sizeof(int64_t),
                                               cudaMemcpyDeviceToHost, reinterpret_cast<cudaStream_t>(stream_ptr)),
-                              "Failed to copy gpu memory.");
+                              "Failed to copy output_shape to host.");
     CHECK_CUDA_RET_WITH_EXCEPT(c_node_ptr_, cudaDeviceSynchronize(), "cudaDeviceSyncFailed");
 
+    LogExceptionIfNotOk(error_code);
+
+    CalRange(range_start, range_end, range_delta, output_device_address, output_shape_device_address,
+             error_code_device_address, max_output_length_, reinterpret_cast<cudaStream_t>(stream_ptr));
+
     return true;
   }
 
+  void LogExceptionIfNotOk(DynamicRangeErrorCode error_code) {
+    switch (error_code) {
+      case DynamicRangeErrorCode::kOk:
+        return;
+      case DynamicRangeErrorCode::kDeltaIsZero:
+        MS_LOG(EXCEPTION) << "gpu RangeOp input error: delta cannot be equal to zero";
+        break;
+      case DynamicRangeErrorCode::kInvalidPositiveDelta:
+        MS_LOG(EXCEPTION) << "gpu RangeOp input error: delta cannot be positive when limit < start";
+        break;
+      case DynamicRangeErrorCode::kInvalidNegativeDelta:
+        MS_LOG(EXCEPTION) << "gpu RangeOp input error: delta cannot be negative when limit > start";
+        break;
+      case DynamicRangeErrorCode::kMaxSizeExceeded:
+        MS_LOG(EXCEPTION) << "gpu RangeOp memory error: the number of elements in the output exceeds maxlen";
+        break;
+      default:
+        MS_LOG(EXCEPTION) << "gpu RangeOp unknown error";
+    }
+  }
+
   void PostExecute() override {
     // required synchronize for PostExecute
     CHECK_CUDA_RET_WITH_EXCEPT(c_node_ptr_, cudaStreamSynchronize(reinterpret_cast<cudaStream_t>(stream_ptr_)),
@@ -103,6 +139,7 @@ class DynamicRangeGpuKernel : public GpuKernel {
 
     // this op outputs a 1d tensor, size of one int64_t is enough space to hold the shape.
     workspace_size_list_.push_back(sizeof(int64_t));
+    workspace_size_list_.push_back(sizeof(DynamicRangeErrorCode));
     return;
   }
 

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/dynamic_range_impl.cu

@@ -1,5 +1,5 @@
 /**
- * Copyright 2021 Huawei Technologies Co., Ltd
+ * Copyright 2020-2021 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.
@@ -20,57 +20,90 @@
 #include "runtime/device/gpu/cuda_common.h"
 
 template <typename T>
-__device__ void CheckInputs(const T &start, const T &end, const T &delta) {
+__global__ void ValidateInputAndInferShape(const T *range_start, const T *range_end, const T *range_delta,
+                                           int64_t *output_shape, DynamicRangeErrorCode *error_code,
+                                           const int64_t max_output_size) {
+  T start = range_start[0];
+  T end = range_end[0];
+  T delta = range_delta[0];
+  *error_code = DynamicRangeErrorCode::kOk;
+
   if (delta == 0) {
-    asm("trap;");
+    *error_code = DynamicRangeErrorCode::kDeltaIsZero;
+    return;
   }
 
   if (start < end && delta < 0) {
-    asm("trap;");
+    *error_code = DynamicRangeErrorCode::kInvalidNegativeDelta;
+    return;
   }
 
   if (start > end && delta > 0) {
-    asm("trap;");
+    *error_code = DynamicRangeErrorCode::kInvalidPositiveDelta;
+    return;
+  }
+
+  if (*error_code == DynamicRangeErrorCode::kOk) {
+    int64_t real_output_shape = static_cast<int64_t>(ceil(static_cast<double>(end - start) / delta));
+    if (real_output_shape > max_output_size) {
+        *error_code = DynamicRangeErrorCode::kMaxSizeExceeded;
+    }
+    *output_shape = real_output_shape;
   }
 }
 
 template <typename T>
-__global__ void Range(const T *range_start, const T *range_end, const T *range_delta, T *output,
-                             int64_t *output_shape, const int64_t max_output_size) {
+__global__ void Range(const T *range_start, const T *range_end, const T *range_delta, T *output, int64_t *output_shape,
+                      const int64_t max_output_size) {
   T start = range_start[0];
-  T end = range_end[0];
   T delta = range_delta[0];
 
-  CheckInputs(start, end, delta);
-
-  int64_t real_output_shape = static_cast<int64_t>(ceil(static_cast<double>(end - start) / delta));
-  if (real_output_shape > max_output_size) {
-    asm("trap;");
-  }
-  *output_shape = real_output_shape;
-
   size_t gt_id = blockIdx.x * blockDim.x + threadIdx.x;
-  for (; gt_id < real_output_shape; gt_id += blockDim.x * gridDim.x) {
+  for (; gt_id < *output_shape; gt_id += blockDim.x * gridDim.x) {
     output[gt_id] = gt_id * delta + start;
   }
 }
 
+template <typename T>
+void CudaValidateInputAndInferShape(const T *range_start, const T *range_end, const T *range_delta,
+                                    int64_t *output_shape, DynamicRangeErrorCode *error_code,
+                                    const int64_t max_output_size, cudaStream_t cuda_stream) {
+  ValidateInputAndInferShape<<<1, 1, 0, cuda_stream>>>(range_start, range_end, range_delta, output_shape, error_code,
+                                                       max_output_size);
+}
+
 template <typename T>
 void CalRange(const T *range_start, const T *range_end, const T *range_delta, T *output, int64_t *output_shape,
-                     const int64_t max_output_size, cudaStream_t cuda_stream) {
+              DynamicRangeErrorCode *error_code, const int64_t max_output_size, cudaStream_t cuda_stream) {
   Range<<<GET_BLOCKS(max_output_size), GET_THREADS, 0, cuda_stream>>>(range_start, range_end, range_delta,
                                                                              output, output_shape, max_output_size);
 }
 
-template void CalRange<int>(const int *range_start, const int *range_end, const int *range_delta, int *output,
-                                   int64_t *output_shape, const int64_t max_output_size, cudaStream_t cuda_stream);
+template void CudaValidateInputAndInferShape<int>(const int *range_start, const int *range_end, const int *range_delta,
+                                                  int64_t *output_shape, DynamicRangeErrorCode *error_code,
+                                                  const int64_t max_output_size, cudaStream_t cuda_stream);
+template void CudaValidateInputAndInferShape<int64_t>(const int64_t *range_start, const int64_t *range_end,
+                                                      const int64_t *range_delta, int64_t *output_shape,
+                                                      DynamicRangeErrorCode *error_code, const int64_t max_output_size,
+                                                      cudaStream_t cuda_stream);
+template void CudaValidateInputAndInferShape<float>(const float *range_start, const float *range_end,
+                                                    const float *range_delta, int64_t *output_shape,
+                                                    DynamicRangeErrorCode *error_code, const int64_t max_output_size,
+                                                    cudaStream_t cuda_stream);
+template void CudaValidateInputAndInferShape<double>(const double *range_start, const double *range_end,
+                                                     const double *range_delta, int64_t *output_shape,
+                                                     DynamicRangeErrorCode *error_code, const int64_t max_output_size,
+                                                     cudaStream_t cuda_stream);
 
+template void CalRange<int>(const int *range_start, const int *range_end, const int *range_delta, int *output,
+                            int64_t *output_shape, DynamicRangeErrorCode *error_code, const int64_t max_output_size,
+                            cudaStream_t cuda_stream);
 template void CalRange<int64_t>(const int64_t *range_start, const int64_t *range_end, const int64_t *range_delta,
-                                int64_t *output, int64_t *output_shape, const int64_t max_output_size,
-                                cudaStream_t cuda_stream);
-
+                                int64_t *output, int64_t *output_shape, DynamicRangeErrorCode *error_code,
+                                const int64_t max_output_size, cudaStream_t cuda_stream);
 template void CalRange<float>(const float *range_start, const float *range_end, const float *range_delta, float *output,
-                              int64_t *output_shape, const int64_t max_output_size, cudaStream_t cuda_stream);
+                              int64_t *output_shape, DynamicRangeErrorCode *error_code, const int64_t max_output_size,
+                              cudaStream_t cuda_stream);
 template void CalRange<double>(const double *range_start, const double *range_end, const double *range_delta,
-                               double *output, int64_t *output_shape, const int64_t max_output_size,
-                               cudaStream_t cuda_stream);
+                               double *output, int64_t *output_shape, DynamicRangeErrorCode *error_code,
+                               const int64_t max_output_size, cudaStream_t cuda_stream);

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/dynamic_range_impl.cuh

@@ -1,5 +1,5 @@
 /**
- * Copyright 2021 Huawei Technologies Co., Ltd
+ * Copyright 2020-2021 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.
@@ -19,8 +19,21 @@
 
 #include <cuda_runtime.h>
 
+enum class DynamicRangeErrorCode {
+  kOk = 0,
+  kDeltaIsZero,
+  kInvalidPositiveDelta,
+  kInvalidNegativeDelta,
+  kMaxSizeExceeded
+};
+
+template <typename T>
+void CudaValidateInputAndInferShape(const T *range_start, const T *range_end, const T *range_delta,
+                                    int64_t *output_shape, DynamicRangeErrorCode *error_code,
+                                    const int64_t max_output_size, cudaStream_t cuda_stream);
+
 template <typename T>
 void CalRange(const T *range_start, const T *range_end, const T *range_delta, T *output, int64_t *output_shape,
-              const int64_t max_output_size, cudaStream_t cuda_stream);
+              DynamicRangeErrorCode *error_code, const int64_t max_output_size, cudaStream_t cuda_stream);
 
 #endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_DYNAMIC_RANGE_CUH_

tests/st/ops/gpu/test_range_op.py

@@ -22,12 +22,12 @@ from mindspore import Tensor
 from mindspore.ops import operations as P
 
 class RangeNet(nn.Cell):
-    def __init__(self):
+    def __init__(self, maxlen=10000):
         super(RangeNet, self).__init__()
-        self.range = P.Range()
+        self.range = P.Range(maxlen)
 
-    def construct(self, s, e, d):
-        return self.range(s, e, d)
+    def construct(self, start, limit, delta):
+        return self.range(start, limit, delta)
 
 
 @pytest.mark.level0
@@ -91,3 +91,27 @@ def test_range_invalid_max_output_length():
         _ = P.Range(-1)
         _ = P.Range(None)
         _ = P.Range('5')
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_range_invalid_input():
+    with pytest.raises(RuntimeError) as info:
+        range_net = RangeNet(3500)
+        _ = range_net(Tensor(0, mstype.int32), Tensor(5, mstype.int32), Tensor(0, mstype.int32)).asnumpy()
+    assert "delta cannot be equal to zero" in str(info.value)
+
+    with pytest.raises(RuntimeError) as info:
+        range_net = RangeNet(2)
+        _ = range_net(Tensor(2, mstype.int32), Tensor(5, mstype.int32), Tensor(1, mstype.int32)).asnumpy()
+    assert "number of elements in the output exceeds maxlen" in str(info.value)
+
+    with pytest.raises(RuntimeError) as info:
+        range_net = RangeNet(3500)
+        _ = range_net(Tensor(20, mstype.int32), Tensor(5, mstype.int32), Tensor(1, mstype.int32)).asnumpy()
+    assert "delta cannot be positive when limit < start" in str(info.value)
+
+    with pytest.raises(RuntimeError) as info:
+        range_net = RangeNet(3500)
+        _ = range_net(Tensor(2, mstype.int32), Tensor(5, mstype.int32), Tensor(-4, mstype.int32)).asnumpy()
+    assert "delta cannot be negative when limit > start" in str(info.value)