diff --git a/mindspore/ops/_op_impl/_custom_op/batch_matmul_impl.py b/mindspore/ops/_op_impl/_custom_op/batch_matmul_impl.py index 97982c53cf..c512989906 100644 --- a/mindspore/ops/_op_impl/_custom_op/batch_matmul_impl.py +++ b/mindspore/ops/_op_impl/_custom_op/batch_matmul_impl.py @@ -164,9 +164,10 @@ def CusBatchMatMul(input_x1, input_x2, output, transpose_a=False, transpose_b=Tr matmul_hybrid_f_t_local_UB = tik_instance.Tensor(dtype, [64], name="matmul_hybrid_f_t_local_UB", scope=tik.scope_ubuf) - matmul_hybrid_f_t_local_UB_dst_tmp = tik_instance.Tensor(dtype, [64], - name="matmul_hybrid_f_t_local_UB_dst_tmp", - scope=tik.scope_ubuf) + matmul_hybrid_f_t_local_UB_dst_tmp = tik_instance.Tensor( + dtype, [64], + name="matmul_hybrid_f_t_local_UB_dst_tmp", + scope=tik.scope_ubuf) tik_instance.vector_dup(64, matmul_hybrid_f_t_local_UB, 0, 1, 1, 8) tik_instance.data_move(input_2_local_UB, input2[(block_idx // 6) * 16384 + thread_idx2 * 8192], 0, 1, diff --git a/mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_left_impl.py b/mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_left_impl.py index e5c380369d..e95a9ba069 100644 --- a/mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_left_impl.py +++ b/mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_left_impl.py @@ -127,7 +127,7 @@ def _shape_check(shape_a, shape_b, shape_bias, src_dtype, trans_a, trans_b): if n_shape % cce.BLOCK_IN != 0 and n_shape != 1: raise RuntimeError("input shape N should be 1 or multiple of %d" % cce.BLOCK_IN) - if len(shape_bias) != 0: + if shape_bias: if len(shape_bias) == 1: if is_gevm or is_gemv: if shape_bias[0] != m_shape * n_shape: @@ -189,7 +189,7 @@ def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, t util.check_shape_size(shape_b, SHAPE_SIZE_LIMIT) try: trans_a_f = bool(1 - trans_a) - if src_dtype == "float32" or src_dtype == "int32": + if src_dtype in ("float32", "int32"): if len(shape_a) != 2 and len(shape_b) != 2: return False if trans_b: @@ -239,6 +239,7 @@ def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, t return False except RuntimeError as e: + print(e) return False return True @@ -385,7 +386,7 @@ def CusMatMulCubeDenseLeft(input_x1, input_x2, bias=None, output_y={}, trans_a=F tensor_b = tvm.placeholder(shape_b_temp, name='tensor_b', dtype=src_dtype) - if len(shape_bias) > 0: + if shape_bias: tensor_bias = tvm.placeholder(shape_bias, name='tensor_bias', dtype=dst_dtype) @@ -449,20 +450,20 @@ def CusMatMulCubeDenseLeft(input_x1, input_x2, bias=None, output_y={}, trans_a=F resMatmul_local_UB, 0, 16, 224 // 2, 0, 56 * 16 * 2 // 2) tik_instance.BuildCCE(kernel_name=kernel_name, inputs=[input_x1, input_x2], outputs=[resMatmul]) return tik_instance - else: - print("come into tbe, shape is error!") - result = te.lang.cce.matmul(tensor_a, tensor_b, trans_a, trans_b, format_a=format_a, - format_b=format_b, dst_dtype=dst_dtype, tensor_bias=tensor_bias) - with tvm.target.cce(): - schedule = generic.auto_schedule(result) + print("come into tbe, shape is error!") + result = te.lang.cce.matmul(tensor_a, tensor_b, trans_a, trans_b, format_a=format_a, + format_b=format_b, dst_dtype=dst_dtype, tensor_bias=tensor_bias) + + with tvm.target.cce(): + schedule = generic.auto_schedule(result) - tensor_list = [tensor_a, tensor_b, result] - if len(shape_bias) > 0: - tensor_list = [tensor_a, tensor_b, tensor_bias, result] + tensor_list = [tensor_a, tensor_b, result] + if shape_bias: + tensor_list = [tensor_a, tensor_b, tensor_bias, result] - config = {"print_ir": False, - "name": kernel_name, - "tensor_list": tensor_list} + config = {"print_ir": False, + "name": kernel_name, + "tensor_list": tensor_list} - te.lang.cce.cce_build_code(schedule, config) + te.lang.cce.cce_build_code(schedule, config) diff --git a/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_left_cast_impl.py b/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_left_cast_impl.py index 11b668445e..d6c0d850b9 100644 --- a/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_left_cast_impl.py +++ b/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_left_cast_impl.py @@ -124,7 +124,7 @@ src_dtype: str if n_shape % cce.BLOCK_IN != 0 and n_shape != 1: raise RuntimeError("input shape N should be 1 or multiple of %d" % cce.BLOCK_IN) - if len(shape_bias): + if shape_bias: if len(shape_bias) == 1: if is_gevm or is_gemv: if shape_bias[0] != m_shape * n_shape: @@ -144,11 +144,10 @@ def _get_bias(shape_bias): bias_length = shape_bias[0] if bias_length % 16 == 0: return shape_bias - else: - bias_length = (bias_length // 16) * 16 + 16 - shape_bias = [] - shape_bias.append(bias_length) - return shape_bias + bias_length = (bias_length // 16) * 16 + 16 + shape_bias = [] + shape_bias.append(bias_length) + return shape_bias def _get_input_shape(shape_x): @@ -184,7 +183,7 @@ def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, t util.check_shape_size(shape_b, SHAPE_SIZE_LIMIT) try: trans_a_f = bool(1 - trans_a) - if src_dtype == "float32" or src_dtype == "int32": + if src_dtype in ("floate32", "int32"): if len(shape_a) != 2 and len(shape_b) != 2: return False if trans_b: @@ -234,6 +233,7 @@ def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, t return False except RuntimeError as e: + print(e) return False return True diff --git a/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_right_mul_impl.py b/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_right_mul_impl.py index 79fab2c3cd..e1ce6d4ede 100644 --- a/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_right_mul_impl.py +++ b/mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_right_mul_impl.py @@ -80,8 +80,8 @@ def CusMatMulCubeFraczRightMul(input_x1, input_x2, input_x3, bias=None, output_y ((32, 128, 16, 16), 'float16', (32, 32, 16, 16), 'float16', (1,), 'float32'), ((64, 32, 16, 16), 'float16', (64, 64, 16, 16), 'float16', (1,), 'float32'), ((16, 64, 16, 16), 'float16', (16, 16, 16, 16), 'float16', (1,), 'float32')] - input_shape = ( - tuple(input_x1_shape), input_x1_dtype, tuple(input_x2_shape), input_x2_dtype, tuple(input_x3_shape), input_x3_dtype) + input_shape = (tuple(input_x1_shape), input_x1_dtype, tuple(input_x2_shape), + input_x2_dtype, tuple(input_x3_shape), input_x3_dtype) if input_shape not in Supported: raise RuntimeError("input_shape %s is not supported" % str(input_shape)) diff --git a/mindspore/ops/_op_impl/_custom_op/matmul_cube_impl.py b/mindspore/ops/_op_impl/_custom_op/matmul_cube_impl.py index 603ed287f6..d14cb0d3c7 100644 --- a/mindspore/ops/_op_impl/_custom_op/matmul_cube_impl.py +++ b/mindspore/ops/_op_impl/_custom_op/matmul_cube_impl.py @@ -129,7 +129,7 @@ def _shape_check(shape_a, shape_b, shape_bias, src_dtype, trans_a, trans_b): if n_shape % cce.BLOCK_IN != 0 and n_shape != 1: raise RuntimeError("input shape N should be 1 or multiple of %d" % cce.BLOCK_IN) - if len(shape_bias): + if shape_bias: if len(shape_bias) == 1: if is_gevm or is_gemv: if shape_bias[0] != m_shape * n_shape: @@ -149,11 +149,10 @@ def _get_bias(shape_bias): bias_length = shape_bias[0] if bias_length % 16 == 0: return shape_bias - else: - bias_length = (bias_length // 16) * 16 + 16 - shape_bias = [] - shape_bias.append(bias_length) - return shape_bias + bias_length = (bias_length // 16) * 16 + 16 + shape_bias = [] + shape_bias.append(bias_length) + return shape_bias def _get_input_shape(shape_x): @@ -189,7 +188,7 @@ def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, t util.check_shape_size(shape_b, SHAPE_SIZE_LIMIT) try: trans_a_f = bool(1 - trans_a) - if src_dtype == "float32" or src_dtype == "int32": + if src_dtype in ("float32", "int32"): if len(shape_a) != 2 and len(shape_b) != 2: return False if trans_b: @@ -239,6 +238,7 @@ def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, t return False except RuntimeError as e: + print(e) return False return True @@ -314,7 +314,7 @@ def CusMatMulCube(input_x1, input_x2, bias=None, output_y={}, trans_a=False, tra src_dtype = input_x1.get("dtype").lower() dst_dtype = output_y.get("dtype").lower() - if src_dtype == "float32" or src_dtype == "int32": + if src_dtype in ("float32", "int32"): matmul_vector_cce(shape_a, shape_b, src_dtype, trans_a, trans_b, shape_bias, kernel_name) return _shape_check(shape_a, shape_b, shape_bias, src_dtype, trans_a, trans_b) @@ -377,7 +377,7 @@ def CusMatMulCube(input_x1, input_x2, bias=None, output_y={}, trans_a=False, tra tensor_b = tvm.placeholder(shape_b_temp, name='tensor_b', dtype=src_dtype) - if len(shape_bias) > 0: + if shape_bias: tensor_bias = tvm.placeholder(shape_bias, name='tensor_bias', dtype=dst_dtype) result = te.lang.cce.matmul(tensor_a, tensor_b, trans_a, trans_b, format_a=format_a, @@ -387,7 +387,7 @@ def CusMatMulCube(input_x1, input_x2, bias=None, output_y={}, trans_a=False, tra schedule = generic.auto_schedule(result) tensor_list = [tensor_a, tensor_b, result] - if len(shape_bias) > 0: + if shape_bias: tensor_list = [tensor_a, tensor_b, tensor_bias, result] config = {"print_ir": False, diff --git a/tests/st/ops/ascend/test_ops_infer.py b/tests/st/ops/ascend/test_ops_infer.py index 43d56c865c..350116eb9f 100644 --- a/tests/st/ops/ascend/test_ops_infer.py +++ b/tests/st/ops/ascend/test_ops_infer.py @@ -16,17 +16,10 @@ import functools import numpy as np import mindspore.nn as nn -import mindspore.context as context import mindspore.common.dtype as mstype -from mindspore import Tensor, Parameter -from mindspore.common.initializer import initializer -from mindspore.ops import Primitive -from mindspore.ops import composite as C +from mindspore import Tensor from mindspore.ops import operations as P -from mindspore.ops import functional as F -from mindspore.ops import prim_attr_register, PrimitiveWithInfer -from mindspore.ops.primitive import constexpr from mindspore import context context.set_context(mode=context.GRAPH_MODE, save_graphs=True) @@ -38,7 +31,7 @@ def test_cast_op_attr(): self.cast = P.Cast() def construct(self, x, t): return self.cast(x, t) - + class CastTypeTest(nn.Cell): def __init__(self, net): super(CastTypeTest, self).__init__() @@ -54,9 +47,9 @@ def test_cast_op_attr(): t5 = cast_net(z, mstype.float16) return (t1, t2, t3, t4, t5) net = CastTypeTest(CastNet()) - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.int32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) - t3 = Tensor(np.ones([1,16,1,1918]).astype(np.int32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.int32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) + t3 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.int32)) out = net(t1, t2, t3) assert out[0].asnumpy().dtype == np.float32 assert out[1].asnumpy().dtype == np.int32 diff --git a/tests/st/ops/ascend/test_tbe_ops/test_mul.py b/tests/st/ops/ascend/test_tbe_ops/test_mul.py index 1018728872..b4e8412135 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_mul.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_mul.py @@ -33,13 +33,13 @@ class Net(nn.Cell): return self.mul(x1, x2) -x1 = np.random.randn(3, 4).astype(np.float32) -x2 = np.random.randn(3, 4).astype(np.float32) +arr_x1 = np.random.randn(3, 4).astype(np.float32) +arr_x2 = np.random.randn(3, 4).astype(np.float32) def test_net(): mul = Net() - output = mul(Tensor(x1), Tensor(x2)) - print(x1) - print(x2) + output = mul(Tensor(arr_x1), Tensor(arr_x2)) + print(arr_x1) + print(arr_x2) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_npu_clear_float_status.py b/tests/st/ops/ascend/test_tbe_ops/test_npu_clear_float_status.py index 3955c049ef..eaba73b100 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_npu_clear_float_status.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_npu_clear_float_status.py @@ -33,11 +33,11 @@ class Net(nn.Cell): return self.npu_clear_float_status(x1) -x1 = np.random.randn(8).astype(np.float32) +arr_x1 = np.random.randn(8).astype(np.float32) def test_net(): npu_clear_float_status = Net() - output = npu_clear_float_status(Tensor(x1)) - print(x1) + output = npu_clear_float_status(Tensor(arr_x1)) + print(arr_x1) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_npu_get_float_status.py b/tests/st/ops/ascend/test_tbe_ops/test_npu_get_float_status.py index 27bf9e3921..78bc5a8410 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_npu_get_float_status.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_npu_get_float_status.py @@ -33,11 +33,11 @@ class Net(nn.Cell): return self.npu_get_float_status(x1) -x1 = np.random.randn(8).astype(np.float32) +arr_x1 = np.random.randn(8).astype(np.float32) def test_net(): npu_get_float_status = Net() - output = npu_get_float_status(Tensor(x1)) - print(x1) + output = npu_get_float_status(Tensor(arr_x1)) + print(arr_x1) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_pad.py b/tests/st/ops/ascend/test_tbe_ops/test_pad.py index 113fcd0eef..2c50d785b6 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_pad.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_pad.py @@ -34,11 +34,11 @@ class Net(nn.Cell): return x -x = np.random.random(size=(2, 2)).astype(np.float32) +arr_x = np.random.random(size=(2, 2)).astype(np.float32) def test_net(): pad = Net() - output = pad(Tensor(x)) + output = pad(Tensor(arr_x)) print("=================output====================") print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_realdiv.py b/tests/st/ops/ascend/test_tbe_ops/test_realdiv.py index f855c27b97..3d08aeee3a 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_realdiv.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_realdiv.py @@ -33,13 +33,13 @@ class Net(nn.Cell): return self.realdiv(x1, x2) -x1 = np.random.randn(3, 4).astype(np.float32) -x2 = np.random.randn(3, 4).astype(np.float32) +arr_x1 = np.random.randn(3, 4).astype(np.float32) +arr_x2 = np.random.randn(3, 4).astype(np.float32) def test_net(): realdiv = Net() - output = realdiv(Tensor(x1), Tensor(x2)) - print(x1) - print(x2) + output = realdiv(Tensor(arr_x1), Tensor(arr_x2)) + print(arr_x1) + print(arr_x2) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_reciprocal.py b/tests/st/ops/ascend/test_tbe_ops/test_reciprocal.py index 9249e24eef..65aef4b207 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_reciprocal.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_reciprocal.py @@ -33,11 +33,11 @@ class Net(nn.Cell): return self.reciprocal(x1) -x1 = np.random.randn(3, 4).astype(np.float32) +arr_x1 = np.random.randn(3, 4).astype(np.float32) def test_net(): reciprocal = Net() - output = reciprocal(Tensor(x1)) - print(x1) + output = reciprocal(Tensor(arr_x1)) + print(arr_x1) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_scatter_nd.py b/tests/st/ops/ascend/test_tbe_ops/test_scatter_nd.py index 982d7951fa..bd91d7cb44 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_scatter_nd.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_scatter_nd.py @@ -31,13 +31,13 @@ class Net(nn.Cell): return self.scatternd(indices, update, (3, 3)) -indices = np.array([[0, 1], [1, 1]]).astype(np.int32) -update = np.array([3.2, 1.1]).astype(np.float32) +arr_indices = np.array([[0, 1], [1, 1]]).astype(np.int32) +arr_update = np.array([3.2, 1.1]).astype(np.float32) def test_net(): scatternd = Net() - print(indices) - print(update) - output = scatternd(Tensor(indices), Tensor(update)) + print(arr_indices) + print(arr_update) + output = scatternd(Tensor(arr_indices), Tensor(arr_update)) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_softmax.py b/tests/st/ops/ascend/test_tbe_ops/test_softmax.py index 07feff5be0..057f0e2465 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_softmax.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_softmax.py @@ -31,11 +31,11 @@ class Net(nn.Cell): return self.Softmax(x) -x = np.array([[5, 1]]).astype(np.float32) +arr_x = np.array([[5, 1]]).astype(np.float32) def test_net(): softmax = Net() - output = softmax(Tensor(x)) - print(x) + output = softmax(Tensor(arr_x)) + print(arr_x) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_split.py b/tests/st/ops/ascend/test_tbe_ops/test_split.py index bed4fdae81..de73a45d72 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_split.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_split.py @@ -31,13 +31,13 @@ class Net(nn.Cell): return self.split(x) -x = np.random.randn(2, 4).astype(np.float32) +arr_x = np.random.randn(2, 4).astype(np.float32) def test_net(): split = Net() - output = split(Tensor(x)) + output = split(Tensor(arr_x)) print("====input========") - print(x) + print(arr_x) print("====output=======") print(output) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_sqrt.py b/tests/st/ops/ascend/test_tbe_ops/test_sqrt.py index 5a61ae9c35..6e06a0126d 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_sqrt.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_sqrt.py @@ -31,11 +31,11 @@ class Net(nn.Cell): return self.sqrt(x) -x = np.array([1.0, 4.0, 9.0]).astype(np.float32) +arr_x = np.array([1.0, 4.0, 9.0]).astype(np.float32) def test_net(): sqrt = Net() - output = sqrt(Tensor(x)) - print(x) + output = sqrt(Tensor(arr_x)) + print(arr_x) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_square.py b/tests/st/ops/ascend/test_tbe_ops/test_square.py index ab6c3a993d..8695035ddf 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_square.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_square.py @@ -31,11 +31,11 @@ class Net(nn.Cell): return self.square(x) -x = np.array([1.0, 4.0, 9.0]).astype(np.float32) +arr_x = np.array([1.0, 4.0, 9.0]).astype(np.float32) def test_net(): square = Net() - output = square(Tensor(x)) - print(x) + output = square(Tensor(arr_x)) + print(arr_x) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_sub.py b/tests/st/ops/ascend/test_tbe_ops/test_sub.py index 77d5302fc5..ce564d3f45 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_sub.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_sub.py @@ -31,13 +31,13 @@ class Net(nn.Cell): return self.sub(x, y) -x = np.random.randn(1, 3, 3, 4).astype(np.float32) -y = np.random.randn(1, 3, 3, 4).astype(np.float32) +arr_x = np.random.randn(1, 3, 3, 4).astype(np.float32) +arr_y = np.random.randn(1, 3, 3, 4).astype(np.float32) def test_net(): sub = Net() - output = sub(Tensor(x), Tensor(y)) - print(x) - print(y) + output = sub(Tensor(arr_x), Tensor(arr_y)) + print(arr_x) + print(arr_y) print(output.asnumpy()) diff --git a/tests/st/ops/ascend/test_tbe_ops/test_tile.py b/tests/st/ops/ascend/test_tbe_ops/test_tile.py index f2a5ed6a87..8dbc4ba3bc 100644 --- a/tests/st/ops/ascend/test_tbe_ops/test_tile.py +++ b/tests/st/ops/ascend/test_tbe_ops/test_tile.py @@ -31,11 +31,11 @@ class Net(nn.Cell): return self.tile(x, (1, 4)) -x = np.array([[0], [1], [2], [3]]).astype(np.int32) +arr_x = np.array([[0], [1], [2], [3]]).astype(np.int32) def test_net(): tile = Net() - print(x) - output = tile(Tensor(x)) + print(arr_x) + output = tile(Tensor(arr_x)) print(output.asnumpy()) diff --git a/tests/st/ops/cpu/test_addn_op.py b/tests/st/ops/cpu/test_addn_op.py index f239313eef..d8cffe0984 100644 --- a/tests/st/ops/cpu/test_addn_op.py +++ b/tests/st/ops/cpu/test_addn_op.py @@ -68,7 +68,7 @@ def test_net_3Input(): addn = Net3I() output = addn(Tensor(x, mstype.float32), Tensor(y, mstype.float32), Tensor(z, mstype.float32)) print("output:\n", output) - expect_result = [[0., 3., 6.], + expect_result = [[0., 3., 6.], [9., 12., 15]] assert (output.asnumpy() == expect_result).all() diff --git a/tests/st/ops/cpu/test_conv2d_backprop_input_op.py b/tests/st/ops/cpu/test_conv2d_backprop_input_op.py index 7945f3828f..f57d24e849 100644 --- a/tests/st/ops/cpu/test_conv2d_backprop_input_op.py +++ b/tests/st/ops/cpu/test_conv2d_backprop_input_op.py @@ -66,7 +66,7 @@ class Net5(nn.Cell): def test_conv2d_backprop_input(): conv2d_input = Net5() output = conv2d_input() - print("================================") + print("================================") # expect output: # [[[[ -5, -4, 5, 12, 0, -8] # [-15, -6, 17, 17, -2, -11] diff --git a/tests/ut/python/ops/test_control_ops.py b/tests/ut/python/ops/test_control_ops.py index 6d41d1cb5b..8697e33fcb 100644 --- a/tests/ut/python/ops/test_control_ops.py +++ b/tests/ut/python/ops/test_control_ops.py @@ -20,7 +20,6 @@ import mindspore as ms from mindspore import Tensor from mindspore import context from mindspore import nn -from mindspore.common.parameter import Parameter, ParameterTuple from mindspore.ops import composite as C from mindspore.ops import functional as F from mindspore.ops import operations as P @@ -447,11 +446,14 @@ def test_index_to_switch_layer(): def test_control_depend_check(): with pytest.raises(TypeError) as e: - depend = P.ControlDepend(0.0) + P.ControlDepend(0.0) + print(e) with pytest.raises(ValueError) as e: - depend = P.ControlDepend(2) + P.ControlDepend(2) + print(e) with pytest.raises(TypeError) as e: - depend = P.ControlDepend((2,)) + P.ControlDepend((2,)) + print(e) def test_if_nested_compile(): @@ -497,7 +499,7 @@ def test_if_inside_for(): c1 = Tensor(1, dtype=ms.int32) c2 = Tensor(1, dtype=ms.int32) net = Net() - out = net(c1, c2) + net(c1, c2) def test_while_in_while(): diff --git a/tests/ut/python/ops/test_nn_ops.py b/tests/ut/python/ops/test_nn_ops.py index 3e258dba30..15ff49e2c0 100644 --- a/tests/ut/python/ops/test_nn_ops.py +++ b/tests/ut/python/ops/test_nn_ops.py @@ -31,7 +31,6 @@ from ....mindspore_test_framework.pipeline.forward.compile_forward \ import pipeline_for_compile_forward_ge_graph_for_case_by_case_config from ....mindspore_test_framework.pipeline.forward.verify_exception \ import pipeline_for_verify_exception_for_case_by_case_config -from mindspore import context context.set_context(mode=context.GRAPH_MODE, save_graphs=True) def conv3x3(in_channels, out_channels, stride=1, padding=1): @@ -382,17 +381,18 @@ def test_conv2d_same_primitive(): class Conv2DSameNet(nn.Cell): def __init__(self): super(Conv2DSameNet, self).__init__() - self.conv1 = nn.Conv2d(16, 64, (1, 41), (1,4), "same", 0, 1, has_bias=True) - self.conv2 = nn.Conv2d(16, 64, (1, 41), (1,4), "same", 0, 1, has_bias=True) + self.conv1 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True) + self.conv2 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True) def construct(self, x, y): r1 = self.conv1(x) r2 = self.conv2(y) return (r1, r2) - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) net = Conv2DSameNet() - out = net(t1, t2) - + net(t1, t2) + + class ComparisonNet(nn.Cell): def __init__(self): """ ComparisonNet definition """ diff --git a/tests/ut/python/ops/test_ops_attr_infer.py b/tests/ut/python/ops/test_ops_attr_infer.py index 95b66e8ff0..92bd23245f 100644 --- a/tests/ut/python/ops/test_ops_attr_infer.py +++ b/tests/ut/python/ops/test_ops_attr_infer.py @@ -13,30 +13,14 @@ # limitations under the License. # ============================================================================ """ test nn ops """ -import functools import numpy as np -import mindspore import mindspore.nn as nn import mindspore.context as context -import mindspore.common.dtype as mstype -from mindspore import Tensor, Parameter -from mindspore.common.initializer import initializer -from mindspore.ops import Primitive -from mindspore.ops import composite as C -from mindspore.ops import operations as P +from mindspore import Tensor from mindspore.ops import functional as F from mindspore.ops import prim_attr_register, PrimitiveWithInfer -from mindspore.ops.primitive import constexpr - -from ..ut_filter import non_graph_engine -from ....mindspore_test_framework.mindspore_test import mindspore_test -from ....mindspore_test_framework.pipeline.forward.compile_forward \ - import pipeline_for_compile_forward_ge_graph_for_case_by_case_config -from ....mindspore_test_framework.pipeline.forward.verify_exception \ - import pipeline_for_verify_exception_for_case_by_case_config -from mindspore import context context.set_context(mode=context.GRAPH_MODE, save_graphs=True) class FakeOp(PrimitiveWithInfer): @@ -57,16 +41,16 @@ def test_conv2d_same_primitive(): class Conv2DSameNet(nn.Cell): def __init__(self): super(Conv2DSameNet, self).__init__() - self.conv1 = nn.Conv2d(16, 64, (1, 41), (1,4), "same", 0, 1, has_bias=True) - self.conv2 = nn.Conv2d(16, 64, (1, 41), (1,4), "same", 0, 1, has_bias=True) + self.conv1 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True) + self.conv2 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True) def construct(self, x, y): r1 = self.conv1(x) r2 = self.conv2(y) return (r1, r2) - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) net = Conv2DSameNet() - out = net(t1, t2) + net(t1, t2) # test cell as high order argument # The graph with free variables used as argument is not supported yet @@ -87,10 +71,10 @@ def Xtest_conv2d_op_with_arg(): a = self.opnet(conv_op, x) b = self.opnet(conv_op, y) return (a, b) - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) net = OpsNet(Conv2dNet()) - out = net(t1, t2) + net(t1, t2) def test_conv2d_op_with_arg(): @@ -115,11 +99,10 @@ def test_conv2d_op_with_arg(): a = self.opnet(op, x, y) b = self.opnet(op, y, x) return (a, b) - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) net = OpsNet(OpNet()) - out = net(t1, t2) - + net(t1, t2) def test_conv2d_op_with_arg_same_input(): @@ -144,10 +127,10 @@ def test_conv2d_op_with_arg_same_input(): a = self.opnet(op, x, x) b = self.opnet(op, y, x) return (a, b) - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) net = OpsNet(OpNet()) - out = net(t1, t2) + net(t1, t2) # test op with partial def test_op_as_partial(): @@ -160,11 +143,11 @@ def test_op_as_partial(): a = partial_op(y) b = partial_op(z) return a, b - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) - t3 = Tensor(np.ones([1,16,1,1234]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) + t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32)) net = OpAsPartial() - out = net(t1, t2, t3) + net(t1, t2, t3) # test op with partial def test_op_as_partial_inside(): @@ -182,13 +165,14 @@ def test_op_as_partial_inside(): super(OuterNet, self).__init__() self.net = OpAsPartial() def construct(self, x, y, z): - a,b = self.net(x, y, z) + a, b = self.net(x, y, z) return a, b - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) - t3 = Tensor(np.ones([1,16,1,1234]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) + t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32)) net = OuterNet() - out = net(t1, t2, t3) + net(t1, t2, t3) + # test op with partial case 2 def test_op_as_partial_independent(): @@ -202,11 +186,12 @@ def test_op_as_partial_independent(): partial_op2 = F.partial(self.op, x) b = partial_op2(z) return a, b - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) - t3 = Tensor(np.ones([1,16,1,1234]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) + t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32)) net = OpAsPartial() - out = net(t1, t2, t3) + net(t1, t2, t3) + def test_nest_partial(): class NestPartial(nn.Cell): @@ -221,11 +206,11 @@ def test_nest_partial(): partial_op4 = F.partial(partial_op3, x) b = partial_op4(z) return a, b - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) - t3 = Tensor(np.ones([1,16,1,1234]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) + t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32)) net = NestPartial() - out = net(t1, t2, t3) + net(t1, t2, t3) # high order argument # op and op args as network arguments @@ -245,11 +230,11 @@ def test_op_with_arg_as_input(): a = self.opnet(op, x, z) b = self.opnet(op, x, y) return (a, b) - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) - t3 = Tensor(np.ones([1,16,1,1234]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) + t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32)) net = OpsNet(WithOpArgNet()) - out = net(t1, t2, t3) + net(t1, t2, t3) # The partial application used as argument is not supported yet # because of the limit of inference specialize system @@ -269,8 +254,8 @@ def Xtest_partial_as_arg(): a = self.partial_net(partial_op, z) b = self.partial_net(partial_op, y) return (a, b) - t1 = Tensor(np.ones([1,16,1,1918]).astype(np.float32)) - t2 = Tensor(np.ones([1,16,1,3840]).astype(np.float32)) - t3 = Tensor(np.ones([1,16,1,1234]).astype(np.float32)) + t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32)) + t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32)) + t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32)) net = OpsNet(PartialArgNet()) - out = net(t1, t2, t3) + net(t1, t2, t3) diff --git a/tests/ut/python/pynative_mode/test_framstruct.py b/tests/ut/python/pynative_mode/test_framstruct.py index bd4b625caa..17db2f5b5d 100644 --- a/tests/ut/python/pynative_mode/test_framstruct.py +++ b/tests/ut/python/pynative_mode/test_framstruct.py @@ -982,7 +982,7 @@ def test_bprop_with_wrong_output_shape(): @bprop_getters.register(BpropWithWrongOutputShape) def get_bprop_with_wrong_output_shape(self): """Generate bprop for BpropWithWrongOutputShape""" - ones = Tensor(np.ones([2, ]).astype(np.int32)) + ones = Tensor(np.ones([2,]).astype(np.int32)) def bprop(x, out, dout): return (ones,)