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https://github.com/zebrajr/pytorch.git
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ffc0c46092
Summary: This diff adds adding metadata to q-dq nodes by inferring the quatization intent from node annotations. Annotations on the node are way for user to specify how a node or subgraph is supposed to be quantized. We continue to use that information to copy metadata on Q/DQ node from appropriate nodes. Test Plan: Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D48488416](https://our.internmc.facebook.com/intern/diff/D48488416) Pull Request resolved: https://github.com/pytorch/pytorch/pull/107107 Approved by: https://github.com/jerryzh168 ghstack dependencies: #107105, #107106, #107899, #107900
368 lines
15 KiB
Python
368 lines
15 KiB
Python
# Owner(s): ["oncall: quantization"]
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import copy
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import unittest
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from typing import List
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import torch
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import torch._export as export
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from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
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from torch.ao.quantization.quantizer import Quantizer
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from torch.ao.quantization.quantizer.xnnpack_quantizer import (
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get_symmetric_quantization_config,
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)
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from torch.ao.quantization.quantizer.xnnpack_quantizer_utils import OP_TO_ANNOTATOR
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from torch.fx import Node
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from torch.testing._internal.common_quantization import QuantizationTestCase
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class TestHelperModules:
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class Conv2dWithObsSharingOps(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.conv = torch.nn.Conv2d(3, 3, 3)
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self.hardtanh = torch.nn.Hardtanh()
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self.adaptive_avg_pool2d = torch.nn.AdaptiveAvgPool2d((1, 1))
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self.linear = torch.nn.Linear(3, 3)
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def forward(self, x):
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x = self.conv(x)
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x = self.adaptive_avg_pool2d(x)
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x = self.hardtanh(x)
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x = x.view(-1, 3)
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x = self.linear(x)
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return x
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def _tag_partitions(
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backend_name: str, op_name: str, annotated_partitions: List[List[Node]]
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):
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for index, partition_nodes in enumerate(annotated_partitions):
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tag_name = backend_name + "_" + op_name + "_" + str(index)
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for node in partition_nodes:
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assert "quantization_tag" not in node.meta, f"{node} is already tagged"
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node.meta["quantization_tag"] = tag_name
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_QUANT_OPS = {
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torch.ops.quantized_decomposed.quantize_per_tensor.default,
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torch.ops.quantized_decomposed.dequantize_per_tensor.default,
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torch.ops.quantized_decomposed.quantize_per_tensor.tensor,
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torch.ops.quantized_decomposed.dequantize_per_tensor.tensor,
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torch.ops.quantized_decomposed.quantize_per_channel.default,
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torch.ops.quantized_decomposed.dequantize_per_channel.default,
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torch.ops.quantized_decomposed.choose_qparams.tensor,
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}
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class TestMetaDataPorting(QuantizationTestCase):
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def _test_metadata_porting(
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self,
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model,
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example_inputs,
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quantizer,
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node_tags=None,
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):
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m_eager = model.eval()
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# program capture
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m = copy.deepcopy(m_eager)
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m = export.capture_pre_autograd_graph(
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m,
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example_inputs,
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)
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m = prepare_pt2e(m, quantizer)
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# Calibrate
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m(*example_inputs)
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m = convert_pt2e(m)
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pt2_quant_output = m(*example_inputs)
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recorded_node_tags = {}
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for n in m.graph.nodes:
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if (
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n.op == "call_function"
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and n.target in _QUANT_OPS
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and "quantization_tag" in n.meta
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):
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if n.target not in recorded_node_tags:
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recorded_node_tags[n.target] = set()
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if n.meta["quantization_tag"] in recorded_node_tags[n.target]:
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raise ValueError(
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f"{n} has tag {n.meta['quantization_tag']} that is associated with another node of the same type"
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)
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recorded_node_tags[n.target].add(n.meta["quantization_tag"])
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self.assertEqual(set(recorded_node_tags.keys()), set(node_tags.keys()))
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for k, v in recorded_node_tags.items():
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self.assertEqual(v, node_tags[k])
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def test_simple_metadata_porting(self):
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"""
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Model under test
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conv2d -> avgpool -> hardtanh -> linear
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Check quantization tags on conv2d, avgpool and linear are correctly set
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"""
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class BackendAQuantizer(Quantizer):
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def annotate(self, gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
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backend_string = "BackendA"
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quantization_config = get_symmetric_quantization_config(
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is_per_channel=True
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)
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annotated_partitions = OP_TO_ANNOTATOR["linear"](
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gm, quantization_config
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)
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_tag_partitions(backend_string, "linear", annotated_partitions)
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annotated_partitions = OP_TO_ANNOTATOR["conv2d"](
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gm, quantization_config
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)
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_tag_partitions(backend_string, "conv2d", annotated_partitions)
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annotated_partitions = OP_TO_ANNOTATOR["adaptive_avg_pool2d"](
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gm, quantization_config
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)
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_tag_partitions(
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backend_string, "adaptive_avg_pool2d", annotated_partitions
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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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example_inputs = (torch.randn(1, 3, 5, 5),)
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quantize_per_tensor_tags = {
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"BackendA_conv2d_0",
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"BackendA_adaptive_avg_pool2d_0",
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"BackendA_linear_0",
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}
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dequantize_per_tensor_tags = {
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"BackendA_adaptive_avg_pool2d_0",
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"BackendA_conv2d_0",
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"BackendA_linear_0",
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}
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dequantize_per_channel_tags = {"BackendA_conv2d_0", "BackendA_linear_0"}
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node_tags = {
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torch.ops.quantized_decomposed.quantize_per_tensor.default: quantize_per_tensor_tags,
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torch.ops.quantized_decomposed.dequantize_per_tensor.default: dequantize_per_tensor_tags,
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torch.ops.quantized_decomposed.dequantize_per_channel.default: dequantize_per_channel_tags,
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}
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self._test_metadata_porting(
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TestHelperModules.Conv2dWithObsSharingOps(),
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example_inputs,
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BackendAQuantizer(),
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node_tags,
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)
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def test_metadata_porting_with_no_quant_inbetween(self):
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"""
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Model under test
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conv2d -> avgpool -> hardtanh -> linear
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Dont quantize avgpool
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Check quantization tags on conv2d and linear are correctly set
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"""
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class BackendAQuantizer(Quantizer):
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def annotate(self, gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
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backend_string = "BackendA"
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quantization_config = get_symmetric_quantization_config(
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is_per_channel=True
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)
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annotated_partitions = OP_TO_ANNOTATOR["linear"](
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gm, quantization_config
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)
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_tag_partitions(backend_string, "linear", annotated_partitions)
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annotated_partitions = OP_TO_ANNOTATOR["conv2d"](
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gm, quantization_config
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)
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_tag_partitions(backend_string, "conv2d", annotated_partitions)
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def validate(self, model: torch.fx.GraphModule) -> None:
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pass
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example_inputs = (torch.randn(1, 3, 5, 5),)
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quantize_per_tensor_tags = {"BackendA_conv2d_0", "BackendA_linear_0"}
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dequantize_per_tensor_tags = {"BackendA_conv2d_0", "BackendA_linear_0"}
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dequantize_per_channel_tags = {"BackendA_conv2d_0", "BackendA_linear_0"}
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node_tags = {
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torch.ops.quantized_decomposed.quantize_per_tensor.default: quantize_per_tensor_tags,
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torch.ops.quantized_decomposed.dequantize_per_tensor.default: dequantize_per_tensor_tags,
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torch.ops.quantized_decomposed.dequantize_per_channel.default: dequantize_per_channel_tags,
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}
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self._test_metadata_porting(
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TestHelperModules.Conv2dWithObsSharingOps(),
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example_inputs,
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BackendAQuantizer(),
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node_tags,
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)
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@unittest.skip("Temporarily disabled")
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def test_metadata_porting_for_dq(self):
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"""
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Model under test
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conv2d -> avgpool -> hardtanh -> linear
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Quantize all except linear.
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Quantize linear with dynamic quantization
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Check quantization tags on conv2d, avgpool and linear are correctly set
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"""
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class BackendAQuantizer(Quantizer):
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def annotate(self, gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
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backend_string = "BackendA"
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# static quantiazation
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quantization_config = get_symmetric_quantization_config(
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is_per_channel=True
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)
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annotated_partitions = OP_TO_ANNOTATOR["conv2d"](
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gm, quantization_config
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)
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_tag_partitions(backend_string, "conv2d", annotated_partitions)
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annotated_partitions = OP_TO_ANNOTATOR["adaptive_avg_pool2d"](
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gm, quantization_config
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)
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_tag_partitions(
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backend_string, "adaptive_avg_pool2d", annotated_partitions
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)
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# dynamic quantization
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quantization_config_dynamic = get_symmetric_quantization_config(
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is_per_channel=True, is_dynamic=True
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)
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annotated_partitions = OP_TO_ANNOTATOR["linear"](
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gm, quantization_config_dynamic
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)
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_tag_partitions(backend_string, "linear_dynamic", annotated_partitions)
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def validate(self, model: torch.fx.GraphModule) -> None:
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pass
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example_inputs = (torch.randn(1, 3, 5, 5),)
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quantize_per_tensor_tags = {
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"BackendA_conv2d_0",
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"BackendA_adaptive_avg_pool2d_0",
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}
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quantize_per_tensor_tensor_tags = {"BackendA_linear_dynamic_0"}
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choose_qparams_tensor_tensor_tags = {"BackendA_linear_dynamic_0"}
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dequantize_per_tensor_tags = {
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"BackendA_adaptive_avg_pool2d_0",
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"BackendA_conv2d_0",
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}
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dequantize_per_tensor_tensor_tags = {"BackendA_linear_dynamic_0"}
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dequantize_per_channel_tags = {
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"BackendA_conv2d_0",
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"BackendA_linear_dynamic_0",
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}
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node_tags = {
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torch.ops.quantized_decomposed.quantize_per_tensor.default: quantize_per_tensor_tags,
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torch.ops.quantized_decomposed.quantize_per_tensor.tensor: quantize_per_tensor_tensor_tags,
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torch.ops.quantized_decomposed.dequantize_per_tensor.default: dequantize_per_tensor_tags,
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torch.ops.quantized_decomposed.dequantize_per_tensor.tensor: dequantize_per_tensor_tensor_tags,
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torch.ops.quantized_decomposed.dequantize_per_channel.default: dequantize_per_channel_tags,
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torch.ops.quantized_decomposed.choose_qparams.tensor: choose_qparams_tensor_tensor_tags,
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}
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self._test_metadata_porting(
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TestHelperModules.Conv2dWithObsSharingOps(),
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example_inputs,
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BackendAQuantizer(),
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node_tags,
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)
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def test_metadata_porting_for_two_dq(self):
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"""
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Model under test
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conv2d -> avgpool -> hardtanh -> linear
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Quantize linear and conv with dynamic quantization
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Check quantization tags on conv2d, avgpool and linear are correctly set
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"""
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class BackendAQuantizer(Quantizer):
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def annotate(self, gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
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backend_string = "BackendA"
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# dynamic quantization
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quantization_config_dynamic = get_symmetric_quantization_config(
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is_per_channel=True, is_dynamic=True
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)
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annotated_partitions = OP_TO_ANNOTATOR["conv2d"](
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gm, quantization_config_dynamic
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)
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_tag_partitions(backend_string, "conv2d_dynamic", annotated_partitions)
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annotated_partitions = OP_TO_ANNOTATOR["linear"](
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gm, quantization_config_dynamic
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)
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_tag_partitions(backend_string, "linear_dynamic", annotated_partitions)
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def validate(self, model: torch.fx.GraphModule) -> None:
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pass
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example_inputs = (torch.randn(1, 3, 5, 5),)
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choose_qparams_tensor_tags = {
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"BackendA_conv2d_dynamic_0",
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"BackendA_linear_dynamic_0",
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}
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quantize_per_tensor_tensor_tags = {
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"BackendA_conv2d_dynamic_0",
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"BackendA_linear_dynamic_0",
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}
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dequantize_per_tensor_tensor_tags = {
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"BackendA_conv2d_dynamic_0",
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"BackendA_linear_dynamic_0",
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}
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dequantize_per_channel_tags = {
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"BackendA_conv2d_dynamic_0",
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"BackendA_linear_dynamic_0",
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}
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node_tags = {
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torch.ops.quantized_decomposed.quantize_per_tensor.tensor: quantize_per_tensor_tensor_tags,
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torch.ops.quantized_decomposed.dequantize_per_tensor.tensor: dequantize_per_tensor_tensor_tags,
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torch.ops.quantized_decomposed.dequantize_per_channel.default: dequantize_per_channel_tags,
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torch.ops.quantized_decomposed.choose_qparams.tensor: choose_qparams_tensor_tags,
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}
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self._test_metadata_porting(
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TestHelperModules.Conv2dWithObsSharingOps(),
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example_inputs,
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BackendAQuantizer(),
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node_tags,
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)
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def test_metadata_porting_for_dq_no_static_q(self):
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"""
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Model under test
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conv2d -> avgpool -> hardtanh -> linear
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Dont quantize anything except linear.
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Quantize linear with dynamic quantization
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Check quantization tags on conv2d, avgpool and linear are correctly set
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"""
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class BackendAQuantizer(Quantizer):
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def annotate(self, gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
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backend_string = "BackendA"
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# dynamic quantization
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quantization_config_dynamic = get_symmetric_quantization_config(
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is_per_channel=True, is_dynamic=True
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)
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annotated_partitions = OP_TO_ANNOTATOR["linear"](
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gm, quantization_config_dynamic
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)
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_tag_partitions(backend_string, "linear_dynamic", annotated_partitions)
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def validate(self, model: torch.fx.GraphModule) -> None:
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pass
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example_inputs = (torch.randn(1, 3, 5, 5),)
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choose_qparams_tensor_tags = {"BackendA_linear_dynamic_0"}
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quantize_per_tensor_tensor_tags = {"BackendA_linear_dynamic_0"}
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dequantize_per_tensor_tensor_tags = {"BackendA_linear_dynamic_0"}
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dequantize_per_channel_tags = {"BackendA_linear_dynamic_0"}
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node_tags = {
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torch.ops.quantized_decomposed.quantize_per_tensor.tensor: quantize_per_tensor_tensor_tags,
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torch.ops.quantized_decomposed.dequantize_per_tensor.tensor: dequantize_per_tensor_tensor_tags,
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torch.ops.quantized_decomposed.dequantize_per_channel.default: dequantize_per_channel_tags,
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torch.ops.quantized_decomposed.choose_qparams.tensor: choose_qparams_tensor_tags,
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}
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self._test_metadata_porting(
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TestHelperModules.Conv2dWithObsSharingOps(),
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example_inputs,
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BackendAQuantizer(),
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node_tags,
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)
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