[Unity] Do not import SciPy by default (#16136)

SciPy recently got a few conflicts with numpy and emits warning messages
at loading time.

Author: junrushao

python/tvm/autotvm/tuner/__init__.py

@@ -22,10 +22,7 @@
 """
 
 from . import callback
-
-from .tuner import Tuner
-
-from .index_based_tuner import GridSearchTuner, RandomTuner
 from .ga_tuner import GATuner
+from .index_based_tuner import GridSearchTuner, RandomTuner
+from .tuner import Tuner
 from .xgboost_tuner import XGBTuner
-from .droplet_turner import DropletTuner

python/tvm/relax/dpl/pattern.py

@@ -26,7 +26,7 @@
 import tvm._ffi as tvm_ffi
 from tvm.ir.container import Array
 from tvm.ir.expr import PrimExpr
-from tvm.relay.op import get
+from tvm.ir.op import Op
 
 from ...ir import make_node
 from ...ir.base import Node
@@ -756,7 +756,7 @@ def is_op(op_name: str) -> ExprPattern:
     result: tvm.relax.dpl.ExprPattern
         The resulting ExprPattern
     """
-    op = get(op_name)
+    op = Op.get(op_name)
     return ExprPattern(op)
 
 

python/tvm/relay/analysis/sparse_conv2d.py

@@ -21,11 +21,12 @@
 to block sparse model
 """
 from collections import namedtuple
+
 import numpy as np
-import scipy.sparse as sp
+
 import tvm
-from . import _ffi_api
 
+from . import _ffi_api
 
 SparseAnalysisResult = namedtuple(
     "SparseAnalysisResult",
@@ -79,9 +80,11 @@ def process_params(
     """
 
     # pylint: disable=import-outside-toplevel
-    from tvm.auto_scheduler.search_task import (
+    import scipy.sparse as sp
+
+    from tvm.auto_scheduler.search_task import (  # lazily import to avoid recursive dependency
         register_task_input_buffer,
-    )  # lazily import to avoid recursive dependency
+    )
 
     memo = SparseAnalysisResult(weight_name=[], weight_shape=[])
     weight_names = _search_conv2d_op_weight(expr)

python/tvm/relay/analysis/sparse_dense.py

@@ -21,11 +21,12 @@
 to block sparse model
 """
 from collections import namedtuple
+
 import numpy as np
-import scipy.sparse as sp
+
 import tvm
-from . import _ffi_api
 
+from . import _ffi_api
 
 SparseAnalysisResult = namedtuple(
     "SparseAnalysisResult",
@@ -75,9 +76,11 @@ def process_params(expr, params, block_size, sparsity_threshold):
     """
 
     # pylint: disable=import-outside-toplevel
-    from tvm.auto_scheduler.search_task import (
+    import scipy.sparse as sp
+
+    from tvm.auto_scheduler.search_task import (  # lazily import to avoid recursive dependency
         register_task_input_buffer,
-    )  # lazily import to avoid recursive dependency
+    )
 
     memo = SparseAnalysisResult(weight_name=[], weight_shape=[])
     weight_names = _search_dense_op_weight(expr)

python/tvm/relay/frontend/tensorflow_ops.py

@@ -1481,10 +1481,10 @@ def _impl(inputs, attr, params, mod):
 
 
 def _sparse_tensor_dense_add():
-    # Sparse utility from scipy
-    from scipy.sparse import csr_matrix
-
     def _impl(inputs, attr, params, mod):
+        # Sparse utility from scipy
+        from scipy.sparse import csr_matrix
+
         assert (
             len(inputs) == 4
         ), "There should be 4 input tensors [sparse_indices, sparse_values, sparse_shape, dense]."

python/tvm/relay/qnn/op/legalizations.py

@@ -17,7 +17,7 @@
 # pylint: disable=invalid-name, unused-argument
 """Backend QNN related feature registration"""
 import numpy as np
-from scipy import special
+
 import tvm
 from tvm import relay
 from tvm._ffi.base import TVMError
@@ -78,14 +78,29 @@ def hardswish_func(x):
 register_qnn_unary_op_legalize("qnn.sqrt", np.sqrt)
 register_qnn_unary_op_legalize("qnn.rsqrt", lambda arr: 1 / np.sqrt(arr))
 register_qnn_unary_op_legalize("qnn.exp", np.exp)
-register_qnn_unary_op_legalize("qnn.erf", special.erf)
 register_qnn_unary_op_legalize("qnn.sigmoid", lambda arr: 1 / (1 + np.exp(-arr)))
 register_qnn_unary_op_legalize("qnn.hardswish", hardswish_func)
 register_qnn_unary_op_legalize("qnn.tanh", np.tanh)
 register_qnn_unary_op_legalize("qnn.log", np.log)
 register_qnn_unary_op_legalize("qnn.abs", np.abs)
 
 
+@reg.register_qnn_legalize("qnn.erf")
+def _legalize_qnn_erf(attrs, inputs, types):
+    from scipy import special  # pylint: disable=import-outside-toplevel
+
+    return create_integer_lookup_op(
+        input_arg=inputs[0],
+        floating_point_func=special.erf,
+        in_scale=inputs[1],
+        in_zero_point=inputs[2],
+        out_scale=inputs[3],
+        out_zero_point=inputs[4],
+        in_dtype=types[0].dtype,
+        out_dtype=types[0].dtype,
+    )
+
+
 # Default to None. If overridden by target, this will not be run.
 # Generic QNN Conv2D legalization function.
 @tvm.target.generic_func

python/tvm/topi/arm_cpu/qnn_legalize.py

@@ -30,11 +30,12 @@
 """
 
 import numpy as np
-from scipy.signal import convolve2d
-from tvm.topi.utils import get_const_tuple
+
 from tvm import nd, relay
-from .qnn_alter_op import prev_ops_match, edit_attrs
+from tvm.topi.utils import get_const_tuple
+
 from ..nn import bias_add_legalize
+from .qnn_alter_op import edit_attrs, prev_ops_match
 
 
 def _compute_fixed_conv2d_outputs(requantize_op):
@@ -112,6 +113,7 @@ def _compute_fixed_depthwise_outputs(requantize_op, fixed_channel_inputs):
         an output channel index, and each value is the value that all entries in that output channel
         will have. If the block has no fixed output channels, this dictionary will be empty.
     """
+    from scipy.signal import convolve2d  # pylint: disable=import-outside-toplevel
 
     bias_add_op = requantize_op.args[0]
     depthwise_op = bias_add_op.args[0]

python/tvm/topi/cuda/sparse.py

@@ -17,13 +17,12 @@
 
 """Sparse operators"""
 import numpy as np
-import scipy.sparse as sp
 
 import tvm
 from tvm import relay, te
 
 from .. import nn
-from ..utils import traverse_inline, get_const_tuple, prod, get_const_int, ceil_div
+from ..utils import ceil_div, get_const_int, get_const_tuple, prod, traverse_inline
 from .transform import schedule_transpose_from_existing
 
 
@@ -358,6 +357,8 @@ def schedule_sparse_dense_padded(outs):
 
 def pad_sparse_matrix(matrix, blocksize):
     """Pad rows of sparse matrix matrix so that they are a multiple of blocksize."""
+    import scipy.sparse as sp  # pylint: disable=import-outside-toplevel
+
     assert isinstance(matrix, sp.bsr_matrix)
     new_entries = np.zeros(matrix.shape[0], dtype=matrix.indptr.dtype)
     bsr = matrix.blocksize[0]
@@ -394,6 +395,8 @@ def _alter_sparse_dense_layout(_attrs, inputs, _tinfos, _out_type):
     sparse_dense implementation for one that operates on a padded matrix. We
     also pad the matrix.
     """
+    import scipy.sparse as sp  # pylint: disable=import-outside-toplevel
+
     # TODO(ANSHUMAN87): Handle for sparse_lhs case too
     if (
         isinstance(inputs[1], relay.Constant)

python/tvm/topi/nn/conv3d_transpose.py

@@ -18,11 +18,11 @@
 """Transposed 3D convolution operators (sometimes called Deconvolution)."""
 import tvm
 from tvm import te
-from tvm import relay
+
+from ..utils import simplify
 from .dilate import dilate
 from .pad import pad
 from .utils import get_pad_tuple3d
-from ..utils import simplify
 
 
 def conv3d_transpose_ncdhw(Input, Filter, strides, padding, out_dtype, output_padding):
@@ -143,6 +143,8 @@ def conv3d_transpose_legalize(attrs, inputs, types):
     result : tvm.relay.Expr
         The legalized expr
     """
+    from tvm import relay  # pylint: disable=import-outside-toplevel
+
     if attrs["data_layout"] == "NDHWC":
         data, kernel = inputs
         kernel_layout = attrs["kernel_layout"]