MLBEDSW-7315: Add support for AvgPool with stride_width > 3
* Convert AvgPool with stride_width > 3 and Valid padding to Conv2D to
optimize it to run on NPU.
Change-Id: I06ab412357f0b09b1498f9019a9d1963a324ad34
Signed-off-by: Raul Farkas <raul.farkas@arm.com>
diff --git a/SUPPORTED_OPS.md b/SUPPORTED_OPS.md
index 947b585..fdceb43 100644
--- a/SUPPORTED_OPS.md
+++ b/SUPPORTED_OPS.md
@@ -134,7 +134,8 @@
- Stride values for both width and height must be integer types
- IFM and OFM data types must match
- Kernel filter values for both width and height must be integer types
-- Stride values for both width and height must be in the range [1, 3]
+- Stride width must be greater than or equal to 1.
+ For stride width greater than 3, valid padding needs to be used.
- Kernel filter values for both width and height must be in the range [1, 8]
- VALID padding: Kernel filter height must be in the range [1, 256]
- VALID padding: Product of kernel filter width and height must be in the range [1, 65536]
diff --git a/ethosu/vela/high_level_command_to_npu_op.py b/ethosu/vela/high_level_command_to_npu_op.py
index 9526bd5..79ac392 100644
--- a/ethosu/vela/high_level_command_to_npu_op.py
+++ b/ethosu/vela/high_level_command_to_npu_op.py
@@ -308,6 +308,14 @@
if tens.dtype == DataType.int32 and is_ifm_tensor:
return True
if ps.primary_op.rounding_mode == RoundingMode.AwayZero:
+ if (
+ ps.primary_op.original_type == Op.AvgPool
+ and ps.primary_op.type == Op.Conv2DBias
+ and ps.primary_op.attrs.get("padding", None) == Padding.VALID
+ ):
+ # Force zero point to 0 for AveragePool operators converted to a Conv2DBias with rounding away from
+ # zero.
+ return True
if ps.primary_op.original_type == Op.ResizeBilinear and ps.primary_op.type == Op.DepthwiseConv2DBias:
# Force zero point to 0 for ResizeBilinear operators converted to a DepthwiseConv with rounding away from
# zero. This is because the reference kernel ignores the zero points.
diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py
index 3685c5a..998d94f 100644
--- a/ethosu/vela/operation.py
+++ b/ethosu/vela/operation.py
@@ -615,7 +615,7 @@
is_supported = False
if self.original_type == Op.ResizeBilinear and self.type == Op.DepthwiseConv2DBias:
is_supported = True
- if self.original_type == Op.AvgPool and self.type == Op.DepthwiseConv2DBias:
+ if self.original_type == Op.AvgPool and self.type in (Op.DepthwiseConv2DBias, Op.Conv2DBias):
is_supported = True
if is_supported:
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py
index 76383a4..7890637 100644
--- a/ethosu/vela/tflite_graph_optimiser.py
+++ b/ethosu/vela/tflite_graph_optimiser.py
@@ -949,7 +949,57 @@
return op
-def fixup_strided_conv(op: Operation, arch, nng) -> Operation:
+def convert_avg_pool_to_conv2d(op: Operation, arch, nng) -> Operation:
+ """Convert strided Average Pools with stride >= 4 to Conv2D."""
+ if op.type != Op.AvgPool:
+ return op
+
+ stride_x, stride_y = op.get_kernel_stride()
+ # For strides <= 3 no optimization is needed
+ if stride_x <= 3:
+ return op
+ h, w = op.attrs["filter_height"], op.attrs["filter_width"]
+ inputs = op.inputs[0]
+ shape = inputs.shape
+
+ # Set necessary conv2d attributes
+ op.attrs.update(
+ {
+ "stride_h": stride_y,
+ "stride_w": stride_x,
+ "dilation_h_factor": 1,
+ "dilation_w_factor": 1,
+ "strides": (1, stride_y, stride_x, 1),
+ "dilation": (1, 1, 1, 1),
+ }
+ )
+
+ # Change op type
+ op.type = Op.Conv2DBias
+ op.name += "_conv2d"
+
+ op.rounding_mode = RoundingMode.AwayZero
+ shape = [h, w, 1, op.ofm.shape[-1]]
+ weights = np.full(shape, 1)
+ quant = QuantizationParameters(scale_f32=1 / (h * w), zero_point=0)
+ # Add unit weight tensor
+ op.add_input_tensor(
+ create_const_tensor(
+ "weights",
+ shape,
+ inputs.dtype,
+ weights,
+ quantization=quant,
+ ),
+ )
+ op.weights.values = np.reshape(op.inputs[1].values, shape)
+
+ # Set IFM/OFM shapes after changing op type
+ op.set_ifm_ofm_shapes()
+ return op
+
+
+def fixup_strided_conv(op: Operation, arch, nng):
"""Optimize or fixup strided Conv2DBias
Optimization:
Reduce, when possible, the Conv2DBias stride from N with 1 > N > 4 to 1
@@ -1853,7 +1903,11 @@
if dtype is None:
dtype = DataType.int64 if op.ifm.dtype == DataType.int16 else DataType.int32
bias_tensor = create_const_tensor(op.name + "_bias", [nr_biases], dtype, bias_values)
- op.set_input_tensor(bias_tensor, op.type.info.indices.biases[0])
+ bias_index = op.type.info.indices.biases[0]
+ if bias_index < len(op.inputs):
+ op.set_input_tensor(bias_tensor, bias_index)
+ else:
+ op.add_input_tensor(bias_tensor)
return op
@@ -2349,6 +2403,7 @@
convert_prelu,
convert_mul_max_to_abs_or_lrelu,
convert_lrelu,
+ convert_avg_pool_to_conv2d,
fixup_strided_conv,
convert_hardswish_to_lut,
rewrite_fully_connected_input,
diff --git a/ethosu/vela/tflite_supported_operators.py b/ethosu/vela/tflite_supported_operators.py
index 25b6897..a24eebc 100644
--- a/ethosu/vela/tflite_supported_operators.py
+++ b/ethosu/vela/tflite_supported_operators.py
@@ -220,7 +220,7 @@
# Conv specific ops:
for op_type in TFLiteSupportedOperators.convolution_ops:
- self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_conv_stride)
+ self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_stride_width_no_upper_limit)
# Conv-like checks:
for op_type in TFLiteSupportedOperators.convolution_like_ops:
@@ -244,10 +244,11 @@
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_depth_multiplier)
# Pooling checks:
- for op_type in TFLiteSupportedOperators.pooling_ops:
+ for op_type in TFLiteSupportedOperators.pooling_ops - TFLiteSupportedOperators.avg_pooling_ops:
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_stride_range)
# AVG pooling specific checks:
for op_type in TFLiteSupportedOperators.avg_pooling_ops:
+ self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_stride_range_no_padding)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_filter_range)
self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_filter_height_range_valid_pad)
self.specific_constraints[op_type].append(
@@ -545,7 +546,7 @@
return True, "Op has depth_multiplier=1"
@staticmethod
- def constraint_conv_stride(op):
+ def constraint_stride_width_no_upper_limit(op):
"""Stride width must be greater than or equal to 1.
For stride widths greater than 3, the post-optimization stride needs to be less than or equal to 3.
Stride height must be between 1 and 3."""
@@ -561,6 +562,17 @@
return valid, f"Op has stride WxH as: {w}x{h}"
@staticmethod
+ def constraint_stride_range_no_padding(op):
+ """Stride width must be greater than or equal to 1.
+ For stride width greater than 3, valid padding needs to be used."""
+ w, _ = op.get_kernel_stride()
+ valid, message = TFLiteSupportedOperators.constraint_stride_width_no_upper_limit(op)
+ padding = op.attrs.get("padding", None)
+ is_optimized_with_valid_padding = padding in (None, Padding.VALID) or w <= 3
+ valid = valid and is_optimized_with_valid_padding
+ return valid, f"{message}, padding: {padding}"
+
+ @staticmethod
def constraint_depthwise_conv_stride(op):
"Stride values for both width and height must be between 1 and 3"
w, h = op.get_kernel_stride()
@@ -614,10 +626,11 @@
def constraint_filter_range(cls, op):
"Kernel filter values for both width and height must be in the range [{}, {}]"
if op.attrs["padding"] == Padding.SAME:
+ sw, _ = op.get_kernel_stride()
w = op.kernel.width
h = op.kernel.height
filter_min, filter_max = cls.filter_range
- valid = (filter_min <= w <= filter_max) and (filter_min <= h <= filter_max)
+ valid = ((filter_min <= w <= filter_max) or sw == w) and (filter_min <= h <= filter_max)
return valid, f"Op has kernel filter WxH as: {w}x{h}"
return True, "Op has padding=VALID"