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
View file
Open in desktop
Original file line numberDiff line numberDiff 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
Comment thread
tatwaichong marked this conversation as resolved.
)

## 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
Comment thread
digantdesai marked this conversation as resolved.
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:
Comment thread
tatwaichong marked this conversation as resolved.
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 DownExpand 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 DownExpand 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
Loading
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 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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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

View file


Open in desktop

Original file line numberDiff line numberDiff 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
Comment thread
tatwaichong marked this conversation as resolved.

 )

 ## 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
Comment thread
digantdesai marked this conversation as resolved.

 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:
Comment thread
tatwaichong marked this conversation as resolved.

 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 DownExpand 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 DownExpand 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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if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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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

View file


Open in desktop

Original file line numberDiff line numberDiff 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
Comment thread
tatwaichong marked this conversation as resolved.

 )

 ## 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
Comment thread
digantdesai marked this conversation as resolved.

 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:
Comment thread
tatwaichong marked this conversation as resolved.

 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 DownExpand 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 DownExpand 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
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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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

View file


Open in desktop

Original file line numberDiff line numberDiff 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
Comment thread
tatwaichong marked this conversation as resolved.

 )

 ## 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
Comment thread
digantdesai marked this conversation as resolved.

 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:
Comment thread
tatwaichong marked this conversation as resolved.

 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 DownExpand 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 DownExpand 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
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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

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 Closed

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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

View file


Open in desktop

Original file line numberDiff line numberDiff 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
Comment thread
tatwaichong marked this conversation as resolved.

 )

 ## 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
Comment thread
digantdesai marked this conversation as resolved.

 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:
Comment thread
tatwaichong marked this conversation as resolved.

 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 DownExpand 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 DownExpand 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
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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

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 Closed

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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

View file


Open in desktop

Original file line numberDiff line numberDiff 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
Comment thread
tatwaichong marked this conversation as resolved.

 )

 ## 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
Comment thread
digantdesai marked this conversation as resolved.

 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:
Comment thread
tatwaichong marked this conversation as resolved.

 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 DownExpand 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 DownExpand 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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injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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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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Open in desktop

Original file line numberDiff line numberDiff 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
Comment thread
tatwaichong marked this conversation as resolved.

 )

 ## 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
Comment thread
digantdesai marked this conversation as resolved.

 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:
Comment thread
tatwaichong marked this conversation as resolved.

 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 DownExpand 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 DownExpand 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
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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

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 Closed

tatwaichong
wants to merge
1
commit into

from

 Closed


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

View file


Open in desktop

Original file line numberDiff line numberDiff 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
Comment thread
tatwaichong marked this conversation as resolved.

 )

 ## 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
Comment thread
digantdesai marked this conversation as resolved.

 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:
Comment thread
tatwaichong marked this conversation as resolved.

 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 DownExpand 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 DownExpand 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
Loading