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hongyij pushed a commit to branch unity
in repository https://gitbox.apache.org/repos/asf/tvm.git
The following commit(s) were added to refs/heads/unity by this push:
new 8a2ffee38e [Unity] Do not import SciPy by default (#16136)
8a2ffee38e is described below
commit 8a2ffee38e6020083c857a3aaaecae29d982c5c7
Author: Junru Shao <[email protected]>
AuthorDate: Thu Nov 16 20:36:30 2023 -0800
[Unity] Do not import SciPy by default (#16136)
SciPy recently got a few conflicts with numpy and emits warning messages
at loading time.
---
python/tvm/autotvm/tuner/__init__.py | 7 ++-----
python/tvm/relax/dpl/pattern.py | 4 ++--
python/tvm/relay/analysis/sparse_conv2d.py | 11 +++++++----
python/tvm/relay/analysis/sparse_dense.py | 11 +++++++----
python/tvm/relay/frontend/tensorflow_ops.py | 6 +++---
python/tvm/relay/qnn/op/legalizations.py | 19 +++++++++++++++++--
python/tvm/topi/arm_cpu/qnn_legalize.py | 8 +++++---
python/tvm/topi/cuda/sparse.py | 7 +++++--
python/tvm/topi/nn/conv3d_transpose.py | 6 ++++--
9 files changed, 52 insertions(+), 27 deletions(-)
diff --git a/python/tvm/autotvm/tuner/__init__.py
b/python/tvm/autotvm/tuner/__init__.py
index dd53862d7d..0f849ee73d 100644
--- a/python/tvm/autotvm/tuner/__init__.py
+++ b/python/tvm/autotvm/tuner/__init__.py
@@ -22,10 +22,7 @@ This tuning loop is repeated.
"""
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
diff --git a/python/tvm/relax/dpl/pattern.py b/python/tvm/relax/dpl/pattern.py
index 4fb08c4635..e5670dee4b 100644
--- a/python/tvm/relax/dpl/pattern.py
+++ b/python/tvm/relax/dpl/pattern.py
@@ -26,7 +26,7 @@ import tvm
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)
diff --git a/python/tvm/relay/analysis/sparse_conv2d.py
b/python/tvm/relay/analysis/sparse_conv2d.py
index 1862ded831..043cff989a 100644
--- a/python/tvm/relay/analysis/sparse_conv2d.py
+++ b/python/tvm/relay/analysis/sparse_conv2d.py
@@ -21,11 +21,12 @@ This file contains helper functions for convert dense model
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)
diff --git a/python/tvm/relay/analysis/sparse_dense.py
b/python/tvm/relay/analysis/sparse_dense.py
index 3199360592..16a724813d 100644
--- a/python/tvm/relay/analysis/sparse_dense.py
+++ b/python/tvm/relay/analysis/sparse_dense.py
@@ -21,11 +21,12 @@ This file contains helper functions for convert dense model
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)
diff --git a/python/tvm/relay/frontend/tensorflow_ops.py
b/python/tvm/relay/frontend/tensorflow_ops.py
index e2c3a34252..14171afb3d 100644
--- a/python/tvm/relay/frontend/tensorflow_ops.py
+++ b/python/tvm/relay/frontend/tensorflow_ops.py
@@ -1481,10 +1481,10 @@ def _sparse_segment_mean_with_num_segments():
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]."
diff --git a/python/tvm/relay/qnn/op/legalizations.py
b/python/tvm/relay/qnn/op/legalizations.py
index ce45b2e759..81df386fc2 100644
--- a/python/tvm/relay/qnn/op/legalizations.py
+++ b/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
@@ -78,7 +78,6 @@ 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)
@@ -86,6 +85,22 @@ register_qnn_unary_op_legalize("qnn.log", np.log)
register_qnn_unary_op_legalize("qnn.abs", np.abs)
[email protected]_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
diff --git a/python/tvm/topi/arm_cpu/qnn_legalize.py
b/python/tvm/topi/arm_cpu/qnn_legalize.py
index 2833fbce26..dae869fcb7 100644
--- a/python/tvm/topi/arm_cpu/qnn_legalize.py
+++ b/python/tvm/topi/arm_cpu/qnn_legalize.py
@@ -30,11 +30,12 @@ as there is no downside. This may be possible with Relax,
but I'm unsure.
"""
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]
diff --git a/python/tvm/topi/cuda/sparse.py b/python/tvm/topi/cuda/sparse.py
index 921075601e..cd977fb1b8 100644
--- a/python/tvm/topi/cuda/sparse.py
+++ b/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)
diff --git a/python/tvm/topi/nn/conv3d_transpose.py
b/python/tvm/topi/nn/conv3d_transpose.py
index 2d048f432f..92bcf6ecad 100644
--- a/python/tvm/topi/nn/conv3d_transpose.py
+++ b/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"]
