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Add initial lowering of aten.convolution to tosa.conv2d support #615

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Add initial lowering of aten.convolution to tosa.conv2d support #615

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96 changes: 79 additions & 17 deletions backends/arm/arm_backend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -553,23 +553,39 @@ def preprocess( # noqa: C901
elif exir_ops.edge.aten.convolution.default == node.target:
input, weight, bias, stride, pad, dilation, _, _, group = inputs

# Currently only int8 is supported in quantized types.
actual_out_type = ts.DType.INT8 if is_quant_node else outp.dtype

## Transpose input tensor to NHWC_Order for TOSA
NHWC_Order = [0, 2, 3, 1]
input_transposed = transpose_helper(
tosa_fb, input, NHWC_Order, outp.dtype
tosa_fb, input, NHWC_Order, actual_out_type
)

## CONV2DOp
# Get the attributes of convolution.
attr = ts.TosaSerializerAttribute()
# PAD
pad_attr = [val for val in pad.special for _ in (0, 1)]
# Stride
stride_attr = stride.special
# Dilation
dilation_attr = dilation.special
attr.ConvAttribute(pad_attr, stride_attr, dilation_attr, 0, 0)

# Non-bias case.
if len(node.all_input_nodes) == 2:
# Create a zero bias tensor if not presented
out_channels = weight.shape[0]
bias_name = "bias" + node.name.split("default", 1)[1]
bias = tosa_fb.addConst(
[out_channels],
ts.DType.INT32 if is_quant_node else outp.dtype,
[0] * out_channels,
name=bias_name,
)

if group.number > 1:
assert (
is_quant_node is False
), "quantized depthwise convolution is not supported yet in BI mode"

# Transpose weight to [KH, KW, C, M]
weight_HWCM_Order = [2, 3, 0, 1]
weight_transposed = transpose_helper(
Expand Down Expand Up @@ -600,14 +616,17 @@ def preprocess( # noqa: C901
# Transpose weight to [OC, H, W, IC]
weight_CHWC_Order = [0, 2, 3, 1]
weight_transposed = transpose_helper(
tosa_fb, weight, weight_CHWC_Order, outp.dtype
tosa_fb, weight, weight_CHWC_Order, actual_out_type
)

## TOSA output shape is [NHWO]
NHWO_Order = [0, 2, 3, 1]
out_shape_TOSA_CONV2D = [outp.shape[i] for i in NHWO_Order]

# The output type is int32 when input type is int8.
conv2d_res = tosa_fb.addIntermediate(
out_shape_TOSA_CONV2D, outp.dtype
out_shape_TOSA_CONV2D,
ts.DType.INT32 if is_quant_node else outp.dtype,
)
tosa_fb.addOperator(
TosaOp.Op().CONV2D,
Expand All @@ -624,6 +643,24 @@ def preprocess( # noqa: C901
NOHW_Order = [0, 3, 1, 2]
attr_output_transpose = ts.TosaSerializerAttribute()
attr_output_transpose.TransposeAttribute(NOHW_Order)

# For quantized convolution, rescale the output value back to the same
# integer value domain of the next op. Otherwise return float32 output.
if is_quant_node:
# Get scale_factor from input, weight, and output.
_, input_scale, _, _, _, _ = getNodeArgs(node.args[0])
_, weight_scale, _, _, _, _ = getNodeArgs(node.args[1])
_, output_scale, _, _, _, _ = getNodeArgs(list(node.users)[0])

conv2d_res = tosa_quant_utils.buildRescaleOpConvOutput(
tosa_fb,
conv2d_res,
actual_out_type,
input_scale,
weight_scale,
output_scale,
)

tosa_fb.addOperator(
TosaOp.Op().TRANSPOSE,
[conv2d_res.name],
Expand Down Expand Up @@ -879,7 +916,7 @@ def preprocess( # noqa: C901
p_data = edge_program.state_dict[parameter_name]

assert isinstance(p_data, torch.Tensor), "Expect Attr to be tensor"
weight_values = p_data.detach().numpy()
parameter_values = p_data.detach().numpy()

# Check if they're for quantized nodes
consumer_node = list(node.users)[0]
Expand All @@ -888,14 +925,14 @@ def preprocess( # noqa: C901
consumer_node
)

weight_values_quantized = (
(weight_values / weight_node_scale.number)
parameter_values_quantized = (
(parameter_values / weight_node_scale.number)
+ weight_node_zp.number
).astype(np.int8)
tosa_fb.addConst(
inputs[0].shape,
ts.DType.INT8,
weight_values_quantized,
parameter_values_quantized,
name=out,
)
elif (
Expand All @@ -914,30 +951,55 @@ def preprocess( # noqa: C901
weight_node
)

weight_values_quantized = (
weight_values / (input_node_scale * weight_node_scale)
parameter_values_quantized = (
parameter_values / (input_node_scale * weight_node_scale)
).astype(np.int32)

tosa_fb.addConst(
inputs[0].shape,
ts.DType.INT32,
parameter_values_quantized,
name=out,
)
elif (
consumer_node.target == exir_ops.edge.aten.convolution.default
and list(consumer_node.users)[0].target == tosa_quant_utils.q_op
):
(
input_node,
weight_node,
bias_node,
) = consumer_node.all_input_nodes

input_node_scale, _ = getQuantNodeArgs(input_node)
weight_node_scale, _ = getQuantNodeArgs(weight_node)

bias_scales = input_node_scale * weight_node_scale
parameter_values_quantized = (
parameter_values / bias_scales
).astype(np.int32)

tosa_fb.addConst(
inputs[0].shape,
ts.DType.INT32,
weight_values_quantized,
parameter_values_quantized,
name=out,
)
else:
tosa_fb.addConst(
inputs[0].shape, inputs[0].dtype, weight_values, name=out
inputs[0].shape, inputs[0].dtype, parameter_values, name=out
)

elif out in edge_program.graph_signature.inputs_to_buffers:
parameter_name = edge_program.graph_signature.inputs_to_buffers[
node.name
]
p_data = edge_program.state_dict[parameter_name]

assert isinstance(p_data, torch.Tensor), "Expect Attr to be tensor"
weight_values = p_data.detach().numpy()
buffer_values = p_data.detach().numpy()
tosa_fb.addConst(
inputs[0].shape, inputs[0].dtype, weight_values, name=out
inputs[0].shape, inputs[0].dtype, buffer_values, name=out
)
else:
tensor = ts.TosaSerializerTensor(
Expand Down
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