[Minor] Change file mode 755 -> 644; EOL CRLF -> LF (#13959)

python/tvm/micro/contrib/stm32/__init__.py

@@ -1,20 +1,20 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-"""Module container of STM32 code generator."""
-
-from .emitter import CodeEmitter, get_input_tensor_name, get_output_tensor_name
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Module container of STM32 code generator."""
+
+from .emitter import CodeEmitter, get_input_tensor_name, get_output_tensor_name

python/tvm/topi/hexagon/qnn/adaptive_avg_pool1d.py

@@ -1,120 +1,120 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-""" Compute and schedule for adaptive_avg_pool1d slice op
-
-Following are few notes and assumptions made by the implementation:
-
-Assumptions:
-1) The input is in NCW layout. Distilbert is the only model that calls
-   nn.adaptive_avg_pool1d and the only layout it uses is 'NCW'.
-2) The op takes output_size as an argument and
-   only handles the specialized case where output_size is 1.
-   The argument output_size is used as the value of output_width.
-3) Both input and output dtype is uint8/int8 and
-   quantization parameter is provided to the op.
-4) Input is assumed to always be multiple of fixed chunk 32c64w.
-
-Notes:
-1) If input width is used as output width, there can be two cases:
-    a. If the quantization parameters of input and output are same,
-       it can return the input as output so the op will be a no-op.
-    b. If the quantization parameters of input and output are different,
-       it will essentially be a requantize op.
-2) If output_size is a value besides 1 or input_width,
-   adaptive_avg_pool1d may use dynamic stride and kernel for each output element.
-   When this case occurs, kernel won't be known at compile time. We want to use
-   the generic implementation nn.adaptive_avg_pool1d() for this case.
-"""
-
-from tvm import te
-from tvm import tir
-from ..utils import get_layout_transform_fn, get_fixed_point_value, saturate
-
-
-def adaptive_avg_pool1d(
-    data: te.Tensor,
-    output_size: list,
-    odtype: str,
-    input_zero_point: int,
-    input_scale: float,
-    output_zero_point: int,
-    output_scale: float,
-):
-    """adaptive_avg_pool1d compute"""
-    _, _, inw = data.shape
-
-    out_width = output_size[0]
-
-    n, c = data.shape[:2]
-    oshape = (n, c) + (out_width,)
-
-    # Kernel is same as input_width since output_width is assumed to be 1
-    if out_width == 1:
-        kw_r = inw
-    else:
-        raise RuntimeError(f"Unsupported output_size, {out_width}'")
-
-    if odtype == "uint8":
-        temp_dtype = "uint32"
-    elif odtype == "int8":
-        temp_dtype = "int32"
-    else:
-        raise RuntimeError(f"Unsupported output dtype, {odtype}'")
-
-    scale_with_area = input_scale / (output_scale * int(kw_r))
-    scale_fixed_point, rsh = get_fixed_point_value(scale_with_area, "int16")
-    corr = (output_zero_point << rsh) - input_zero_point * kw_r * scale_fixed_point
-
-    rw_r = te.reduce_axis((0, kw_r), name="rw_r")
-
-    sum_compute = te.compute(
-        oshape,
-        lambda n, c, w: te.sum(data[n, c, w + rw_r].astype(temp_dtype), axis=[rw_r]),
-        name="sum",
-    )
-
-    avg_compute = te.compute(
-        oshape,
-        lambda n, c, w: saturate(
-            ((sum_compute[n, c, w] * scale_fixed_point) + corr) >> rsh, odtype
-        ).astype(odtype),
-        name="adaptive_avg_1d",
-    )
-    return avg_compute
-
-
-def stir_schedule_ncw_32c64w(outs, ins, input_layout: str):
-    """Schedule for input layout ncw-32c64w and output layout ncw"""
-    func = te.create_prim_func([ins, outs])
-    s = tir.Schedule(func)
-
-    sum_block = s.get_block("sum")
-
-    # Input is multiple of fixed chunk but output is NxCx1
-    # Hence transform_layout is only applied on input
-    input_transformed_layout = get_layout_transform_fn(input_layout)
-    s.transform_layout(sum_block, buffer=("read", 0), index_map=input_transformed_layout)
-
-    return s
-
-
-def tir_adaptive_avg_pool1d_schedule(outs, ins, output_layout: str, input_layout: str):
-    """STIR based schedule"""
-    if output_layout == "ncw":
-        return stir_schedule_ncw_32c64w(outs, ins, input_layout)
-    raise RuntimeError(f"Unexpected layout '{output_layout}'")
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+""" Compute and schedule for adaptive_avg_pool1d slice op
+
+Following are few notes and assumptions made by the implementation:
+
+Assumptions:
+1) The input is in NCW layout. Distilbert is the only model that calls
+   nn.adaptive_avg_pool1d and the only layout it uses is 'NCW'.
+2) The op takes output_size as an argument and
+   only handles the specialized case where output_size is 1.
+   The argument output_size is used as the value of output_width.
+3) Both input and output dtype is uint8/int8 and
+   quantization parameter is provided to the op.
+4) Input is assumed to always be multiple of fixed chunk 32c64w.
+
+Notes:
+1) If input width is used as output width, there can be two cases:
+    a. If the quantization parameters of input and output are same,
+       it can return the input as output so the op will be a no-op.
+    b. If the quantization parameters of input and output are different,
+       it will essentially be a requantize op.
+2) If output_size is a value besides 1 or input_width,
+   adaptive_avg_pool1d may use dynamic stride and kernel for each output element.
+   When this case occurs, kernel won't be known at compile time. We want to use
+   the generic implementation nn.adaptive_avg_pool1d() for this case.
+"""
+
+from tvm import te
+from tvm import tir
+from ..utils import get_layout_transform_fn, get_fixed_point_value, saturate
+
+
+def adaptive_avg_pool1d(
+    data: te.Tensor,
+    output_size: list,
+    odtype: str,
+    input_zero_point: int,
+    input_scale: float,
+    output_zero_point: int,
+    output_scale: float,
+):
+    """adaptive_avg_pool1d compute"""
+    _, _, inw = data.shape
+
+    out_width = output_size[0]
+
+    n, c = data.shape[:2]
+    oshape = (n, c) + (out_width,)
+
+    # Kernel is same as input_width since output_width is assumed to be 1
+    if out_width == 1:
+        kw_r = inw
+    else:
+        raise RuntimeError(f"Unsupported output_size, {out_width}'")
+
+    if odtype == "uint8":
+        temp_dtype = "uint32"
+    elif odtype == "int8":
+        temp_dtype = "int32"
+    else:
+        raise RuntimeError(f"Unsupported output dtype, {odtype}'")
+
+    scale_with_area = input_scale / (output_scale * int(kw_r))
+    scale_fixed_point, rsh = get_fixed_point_value(scale_with_area, "int16")
+    corr = (output_zero_point << rsh) - input_zero_point * kw_r * scale_fixed_point
+
+    rw_r = te.reduce_axis((0, kw_r), name="rw_r")
+
+    sum_compute = te.compute(
+        oshape,
+        lambda n, c, w: te.sum(data[n, c, w + rw_r].astype(temp_dtype), axis=[rw_r]),
+        name="sum",
+    )
+
+    avg_compute = te.compute(
+        oshape,
+        lambda n, c, w: saturate(
+            ((sum_compute[n, c, w] * scale_fixed_point) + corr) >> rsh, odtype
+        ).astype(odtype),
+        name="adaptive_avg_1d",
+    )
+    return avg_compute
+
+
+def stir_schedule_ncw_32c64w(outs, ins, input_layout: str):
+    """Schedule for input layout ncw-32c64w and output layout ncw"""
+    func = te.create_prim_func([ins, outs])
+    s = tir.Schedule(func)
+
+    sum_block = s.get_block("sum")
+
+    # Input is multiple of fixed chunk but output is NxCx1
+    # Hence transform_layout is only applied on input
+    input_transformed_layout = get_layout_transform_fn(input_layout)
+    s.transform_layout(sum_block, buffer=("read", 0), index_map=input_transformed_layout)
+
+    return s
+
+
+def tir_adaptive_avg_pool1d_schedule(outs, ins, output_layout: str, input_layout: str):
+    """STIR based schedule"""
+    if output_layout == "ncw":
+        return stir_schedule_ncw_32c64w(outs, ins, input_layout)
+    raise RuntimeError(f"Unexpected layout '{output_layout}'")