forked from mindspore-Ecosystem/mindspore
!42263 Code Warning Clean
Merge pull request !42263 from archer2049/r1.9
This commit is contained in:
commit
e17f1d691f
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@ -13,6 +13,7 @@
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# limitations under the License.
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# ============================================================================
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"""batch_matmul_impl"""
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from __future__ import absolute_import
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from te import tik
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from topi.cce import util
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@ -13,9 +13,11 @@
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# limitations under the License.
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# ============================================================================
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"""CusCholeskyTrsm"""
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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from __future__ import absolute_import
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from te import tik
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from topi.cce import util
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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cus_cholesky_trsm_op_info = TBERegOp("CusCholeskyTrsm") \
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.fusion_type("OPAQUE") \
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@ -84,7 +86,10 @@ def cus_cholesky_trsm(input_x, output, kernel_name):
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chol_diag_element_final = tik_instance.Scalar("float32")
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chol_diag_element_final.set_as(input_x_ub[split_dim * split_dim - 1])
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trsm_diag_element = tik_instance.Scalar("float32")
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try:
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trsm_diag_element.set_as(1.0 / chol_diag_element_final)
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except ZeroDivisionError:
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assert False, "chol_diag_element_final as a divisor should not be zero."
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temp_ub.__setitem__(split_dim * split_dim - 1, trsm_diag_element)
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with tik_instance.for_range(1, split_dim) as i:
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@ -99,7 +104,10 @@ def cus_cholesky_trsm(input_x, output, kernel_name):
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temp_scalar = tik_instance.Scalar("float32")
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temp_scalar.set_as(input_x_ub[index, index])
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chol_diag_element = tik_instance.Scalar("float32")
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try:
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chol_diag_element.set_as(1.0 / temp_scalar)
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except ZeroDivisionError:
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assert False, "temp_scalar as a divisor should not be zero."
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tik_instance.vsub(64, temp_ub[index, 0], temp_ub[index, 0], assist_1_ub, vector_repeat_times,
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1, 1, 1, 8, 8, 8)
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tik_instance.vmuls(64, temp_ub[index, 0], temp_ub[index, 0], chol_diag_element, vector_repeat_times, 1, 1,
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@ -13,6 +13,8 @@
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# limitations under the License.
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# ============================================================================
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"""CusFusedAbsMax1"""
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from __future__ import absolute_import
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from te import tik
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from topi.cce import util
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@ -13,9 +13,11 @@
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# limitations under the License.
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# ============================================================================
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"""CusImg2ColNC1HWC0"""
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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from __future__ import absolute_import
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from te import tik
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from topi.cce import util
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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cus_img2col_info = TBERegOp("CusImg2Col") \
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.fusion_type("OPAQUE") \
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@ -17,14 +17,14 @@ limitations under the License.
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matmul
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"""
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from __future__ import absolute_import
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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from mindspore.ops._op_impl._custom_op._basic import _shape_check, _get_bias, _get_input_shape
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import te.lang.cce
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import te.platform.cce_params as cce
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from te import tik
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from te import tvm
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from topi import generic
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from topi.cce import util
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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from mindspore.ops._op_impl._custom_op._basic import _shape_check, _get_bias, _get_input_shape
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# General limitation of the size for input shape: 2**31
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SHAPE_SIZE_LIMIT = 2147483648
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@ -19,9 +19,9 @@ matmul
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"""
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from __future__ import absolute_import
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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from te import tik
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from topi.cce import util
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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matmul_cube_dense_right_op_info = TBERegOp("CusMatMulCubeDenseRight") \
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.fusion_type("OPAQUE") \
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@ -17,11 +17,14 @@ limitations under the License.
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matmul
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"""
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from __future__ import absolute_import
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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from mindspore.ops._op_impl._custom_op._basic import _shape_check, _get_bias, _get_input_shape
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import collections
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import te.platform.cce_params as cce
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from te import tik
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from topi.cce import util
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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from mindspore.ops._op_impl._custom_op._basic import _shape_check, _get_bias, _get_input_shape
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# General limitation of the size for input shape: 2**31
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SHAPE_SIZE_LIMIT = 2147483648
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@ -218,7 +221,8 @@ def get_cus_tile_info(input_x1, input_x2, diag_size):
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if shape_info not in tile_map:
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raise ValueError("shape %s is not supported" % str(shape_info))
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mo_tile, ko_tile, no_tile = tile_map[shape_info]
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return mo_tile, ko_tile, no_tile, diag_opt
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cus_tile_info = collections.namedtuple('cus_tile_info', ['mo_tile', 'ko_tile', 'no_tile', 'diag_opt'])
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return cus_tile_info(mo_tile, ko_tile, no_tile, diag_opt)
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def cus_cube_matmul_cast(tik_instance, input_x1, trans_a, input_x2, trans_b,
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@ -19,13 +19,17 @@ matmul
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"""
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from __future__ import absolute_import
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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from collections import namedtuple
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import logging
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from te import tik
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from topi.cce import util
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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# General limitation of the size for input shape: 2**31
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SHAPE_SIZE_LIMIT = 2147483648
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NoneType = type(None)
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logging.basicConfig(level=logging.DEBUG, format="%(asctime)s %(levelname)s %(message)s")
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cus_matmul_cube_fracz_right_mul_op_info = TBERegOp("CusMatMulCubeFraczRightMul") \
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.fusion_type("OPAQUE") \
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mo_tile_ = mo_tile_ // 2
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else:
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raise ValueError("please add tile config to the tile_map")
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print("shape: %s, tile: %s" % (input_shape, str((mo_tile_, ko_tile_, no_tile_, core_m_num_, core_n_num_,
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diag_opt))))
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return mo_tile_, ko_tile_, no_tile_, core_m_num_, core_n_num_, diag_opt
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logging.info(
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"shape: %s, tile: %s", input_shape, str((mo_tile_, ko_tile_, no_tile_, core_m_num_, core_n_num_,
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diag_opt)))
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cus_tile_info = namedtuple('cus_tile_info', ['mo_tile_', 'ko_tile_', 'no_tile_', 'core_m_num_',
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'core_n_num_', 'diag_opt'])
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return cus_tile_info(mo_tile_, ko_tile_, no_tile_, core_m_num_, core_n_num_, diag_opt)
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def cus_cube_matmul_right_mul(tik_instance, input_x1, input_x2, input_x3,
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@ -19,13 +19,13 @@ matmul
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"""
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from __future__ import absolute_import
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from impl.matmul_vector import matmul_vector_cce
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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from mindspore.ops._op_impl._custom_op._basic import _shape_check, _get_bias, _get_input_shape
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import te.lang.cce
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import te.platform.cce_params as cce
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import te.lang.cce
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from te import tvm
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from topi import generic
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from topi.cce import util
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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from mindspore.ops._op_impl._custom_op._basic import _shape_check, _get_bias, _get_input_shape
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# General limitation of the size for input shape: 2**31
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SHAPE_SIZE_LIMIT = 2147483648
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@ -13,9 +13,11 @@
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# limitations under the License.
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# ============================================================================
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"""CusMatrixCombine"""
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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from __future__ import absolute_import
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from te import tik
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from topi.cce import util
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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cus_matrix_combine_op_info = TBERegOp("CusMatrixCombine") \
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.fusion_type("OPAQUE") \
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# limitations under the License.
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# ============================================================================
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"""CusTranspose02314"""
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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from __future__ import absolute_import
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from te import tik
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from topi.cce import util
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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cus_transpose02314_op_info = TBERegOp("CusTranspose02314") \
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.fusion_type("OPAQUE") \
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