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Enable {conv3d, conv_transpose3d} + bn fusion in pt2e (#2212)
* Enable {conv3d, conv_transpose3d} + bn fusion in pt2e Summary: att, previously only 1d and 2d fusion are supported, this PR adds 3d support Test Plan: python test/quantization/pt2e/test_quantize_pt2e.py -k test_conv3d_bn_relu python test/quantization/pt2e/test_quantize_pt2e.py -k test_conv_transpose3d_bn_relu Reviewers: Subscribers: Tasks: Tags: * comment * fix test
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‎test/quantization/pt2e/test_quantize_pt2e.py‎

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Original file line numberDiff line numberDiff line change
@@ -2385,6 +2385,192 @@ def validate(self, model: torch.fx.GraphModule) -> None:
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node_list,
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)
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def test_conv3d_bn_relu(self):
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class BackendAQuantizer(Quantizer):
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def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
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act_qspec = QuantizationSpec(
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dtype=torch.uint8,
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quant_min=0,
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quant_max=255,
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qscheme=torch.per_tensor_affine,
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is_dynamic=False,
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observer_or_fake_quant_ctr=observer.default_observer,
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)
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weight_qspec = QuantizationSpec(
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dtype=torch.int8,
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quant_min=-128,
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quant_max=127,
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qscheme=torch.per_tensor_affine,
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is_dynamic=False,
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observer_or_fake_quant_ctr=observer.default_weight_observer,
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)
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bias_qspec = QuantizationSpec(
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dtype=torch.float32,
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is_dynamic=False,
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observer_or_fake_quant_ctr=observer.PlaceholderObserver,
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)
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# conv_transpose + bn is fused automatically in PTQ (not configurable)
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# so we just need to annotate conv + relu for conv + bn + relu
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# pattern
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for n in model.graph.nodes:
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if (
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n.op != "call_function"
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or n.target != torch.ops.aten.relu.default
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):
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continue
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relu_node = n
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n = n.args[0]
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if (
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n.op != "call_function"
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and n.target != torch.ops.aten.conv3d.input
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):
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continue
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conv_t_node = n
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input_act = conv_t_node.args[0]
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weight = conv_t_node.args[1]
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bias = conv_t_node.args[2]
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conv_t_node.meta["quantization_annotation"] = (
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QuantizationAnnotation(
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input_qspec_map={
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input_act: act_qspec,
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weight: weight_qspec,
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bias: bias_qspec,
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},
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_annotated=True,
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)
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)
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relu_node.meta["quantization_annotation"] = QuantizationAnnotation(
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output_qspec=act_qspec,
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_annotated=True,
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)
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def validate(self, model: torch.fx.GraphModule) -> None:
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pass
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class M(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.conv = torch.nn.Conv3d(2, 2, 3, padding=1)
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self.bn = torch.nn.BatchNorm3d(2)
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def forward(self, x):
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return torch.nn.functional.relu(self.bn(self.conv(x)))
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example_inputs = (torch.randn(1, 2, 2, 5, 5),)
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node_occurrence = {
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# two for input of the first conv, one for output for the first conv
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torch.ops.quantized_decomposed.quantize_per_tensor.default: 2,
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torch.ops.quantized_decomposed.dequantize_per_tensor.default: 3,
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}
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node_list = [
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torch.ops.quantized_decomposed.dequantize_per_tensor.default,
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torch.ops.quantized_decomposed.dequantize_per_tensor.default,
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torch.ops.aten.conv3d.default,
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torch.ops.aten.relu.default,
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torch.ops.quantized_decomposed.quantize_per_tensor.default,
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]
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model = M().eval()
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self._test_quantizer(
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model,
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example_inputs,
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BackendAQuantizer(),
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node_occurrence,
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node_list,
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)
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def test_conv_transpose3d_bn_relu(self):
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class BackendAQuantizer(Quantizer):
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def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
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act_qspec = QuantizationSpec(
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dtype=torch.uint8,
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quant_min=0,
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quant_max=255,
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qscheme=torch.per_tensor_affine,
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is_dynamic=False,
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observer_or_fake_quant_ctr=observer.default_observer,
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)
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weight_qspec = QuantizationSpec(
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dtype=torch.int8,
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quant_min=-128,
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quant_max=127,
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qscheme=torch.per_tensor_affine,
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is_dynamic=False,
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observer_or_fake_quant_ctr=observer.default_weight_observer,
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)
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bias_qspec = QuantizationSpec(
2501+
dtype=torch.float32,
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is_dynamic=False,
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observer_or_fake_quant_ctr=observer.PlaceholderObserver,
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)
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# conv_transpose + bn is fused automatically in PTQ (not configurable)
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# so we just need to annotate conv_transpose + relu for conv_transpose + bn + relu
2507+
# pattern
2508+
for n in model.graph.nodes:
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if (
2510+
n.op != "call_function"
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or n.target != torch.ops.aten.relu.default
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):
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continue
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relu_node = n
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n = n.args[0]
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if (
2517+
n.op != "call_function"
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and n.target != torch.ops.aten.conv_transposed3d.input
2519+
):
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continue
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conv_t_node = n
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input_act = conv_t_node.args[0]
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weight = conv_t_node.args[1]
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bias = conv_t_node.args[2]
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conv_t_node.meta["quantization_annotation"] = (
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QuantizationAnnotation(
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input_qspec_map={
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input_act: act_qspec,
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weight: weight_qspec,
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bias: bias_qspec,
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},
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_annotated=True,
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)
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)
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relu_node.meta["quantization_annotation"] = QuantizationAnnotation(
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output_qspec=act_qspec,
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_annotated=True,
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)
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def validate(self, model: torch.fx.GraphModule) -> None:
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pass
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class M(torch.nn.Module):
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def __init__(self):
2545+
super().__init__()
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self.conv_t = torch.nn.ConvTranspose3d(2, 2, 3, padding=1)
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self.bn = torch.nn.BatchNorm3d(2)
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def forward(self, x):
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return torch.nn.functional.relu(self.bn(self.conv_t(x)))
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example_inputs = (torch.randn(1, 2, 2, 5, 5),)
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node_occurrence = {
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# two for input of the first conv, one for output for the first conv
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torch.ops.quantized_decomposed.quantize_per_tensor.default: 2,
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torch.ops.quantized_decomposed.dequantize_per_tensor.default: 3,
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}
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node_list = [
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torch.ops.quantized_decomposed.dequantize_per_tensor.default,
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torch.ops.quantized_decomposed.dequantize_per_tensor.default,
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torch.ops.aten.conv_transpose3d.input,
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torch.ops.aten.relu.default,
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torch.ops.quantized_decomposed.quantize_per_tensor.default,
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]
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model = M().eval()
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self._test_quantizer(
2567+
model,
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example_inputs,
2569+
BackendAQuantizer(),
2570+
node_occurrence,
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node_list,
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)
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23882574
def test_multi_users_without_output_observer(self):
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"""
23902576
Test the case in which a node is used by multiple users,

‎torchao/quantization/pt2e/utils.py‎

Lines changed: 4 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -626,6 +626,7 @@ def _is_conv_node(n: Node):
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return n.op == "call_function" and n.target in [
627627
torch.ops.aten.conv1d.default,
628628
torch.ops.aten.conv2d.default,
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torch.ops.aten.conv3d.default,
629630
]
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631632

@@ -638,6 +639,8 @@ def _is_conv_transpose_node(n: Node):
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torch.ops.aten.conv_transpose1d.default,
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torch.ops.aten.conv_transpose2d,
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torch.ops.aten.conv_transpose2d.input,
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torch.ops.aten.conv_transpose3d,
643+
torch.ops.aten.conv_transpose3d.input,
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]
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643646

@@ -649,7 +652,7 @@ def _is_conv_or_conv_transpose_node(n: Node):
649652

650653

651654
def _is_conv_transpose_fn(conv_fn: Callable):
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return conv_fn in [F.conv_transpose1d, F.conv_transpose2d]
655+
return conv_fn in [F.conv_transpose1d, F.conv_transpose2d, F.conv_transpose3d]
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655658
def _is_bn_node(n: Node):

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