[ET-VK] Introduce generic export pass for fusing Q/DQ nodes#10525
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facebook-github-bot merged 5 commits intogh/SS-JIA/220/basefrom May 8, 2025
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[ET-VK] Introduce generic export pass for fusing Q/DQ nodes#10525facebook-github-bot merged 5 commits intogh/SS-JIA/220/basefrom
facebook-github-bot merged 5 commits intogh/SS-JIA/220/basefrom
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## Context When quantizing models with the PT2E quantization flow, quantize/dequantize nodes will be inserted into the graph. However, these quantize/dequantize nodes must be fused with operators such as `aten.linear.default` to produce nodes corresponding to quantized operators (e.g. `weight_int8pack_mm`) in order for quantized operator implementations to be called at runtime. Currently, the op fusion is done by the `fuse_dequant_linear.py` pass, however, this only handles one specific fusion pattern to generate a `weight_int8pack_mm` operator. As more quantized operators are to be supported in ET-VK via the PT2E quantization flow, a more generic fusion pass is needed that can handle a variety of fusion patterns. ## Changes Introduce the `FuseQuantizedOpsTransform()` pass. I elected to introduce a new pass under the `backends/vulkan/_passes` directory, as opposed to modifying the existing pass because I anticipate the majority of the fusion patterns to be specific to ET-VK. Remove the existing `FuseDequantLinearPass()` Switch to using the `FuseQuantizedOpsTransform` pass instead of the old `FuseDequantLinear` pass. Add `test_vulkan_passes` Python test to test export passes. Some small refactors to `test_vulkan_delegate` Python test to improve code organizations. Differential Revision: [D73794042](https://our.internmc.facebook.com/intern/diff/D73794042/) [ghstack-poisoned]
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## Context When quantizing models with the PT2E quantization flow, quantize/dequantize nodes will be inserted into the graph. However, these quantize/dequantize nodes must be fused with operators such as `aten.linear.default` to produce nodes corresponding to quantized operators (e.g. `weight_int8pack_mm`) in order for quantized operator implementations to be called at runtime. Currently, the op fusion is done by the `fuse_dequant_linear.py` pass, however, this only handles one specific fusion pattern to generate a `weight_int8pack_mm` operator. As more quantized operators are to be supported in ET-VK via the PT2E quantization flow, a more generic fusion pass is needed that can handle a variety of fusion patterns. ## Changes Introduce the `FuseQuantizedOpsTransform()` pass. I elected to introduce a new pass under the `backends/vulkan/_passes` directory, as opposed to modifying the existing pass because I anticipate the majority of the fusion patterns to be specific to ET-VK. Remove the existing `FuseDequantLinearPass()` Switch to using the `FuseQuantizedOpsTransform` pass instead of the old `FuseDequantLinear` pass. Add `test_vulkan_passes` Python test to test export passes. Some small refactors to `test_vulkan_delegate` Python test to improve code organizations. Differential Revision: [D73794042](https://our.internmc.facebook.com/intern/diff/D73794042/) ghstack-source-id: 280746102 Pull Request resolved: #10525
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/10525
Note: Links to docs will display an error until the docs builds have been completed. ❌ 1 New Failure, 2 Unrelated FailuresAs of commit d817493 with merge base 6932baf ( NEW FAILURE - The following job has failed:
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## Context When quantizing models with the PT2E quantization flow, quantize/dequantize nodes will be inserted into the graph. However, these quantize/dequantize nodes must be fused with operators such as `aten.linear.default` to produce nodes corresponding to quantized operators (e.g. `weight_int8pack_mm`) in order for quantized operator implementations to be called at runtime. Currently, the op fusion is done by the `fuse_dequant_linear.py` pass, however, this only handles one specific fusion pattern to generate a `weight_int8pack_mm` operator. As more quantized operators are to be supported in ET-VK via the PT2E quantization flow, a more generic fusion pass is needed that can handle a variety of fusion patterns. ## Changes Introduce the `FuseQuantizedOpsTransform()` pass. I elected to introduce a new pass under the `backends/vulkan/_passes` directory, as opposed to modifying the existing pass because I anticipate the majority of the fusion patterns to be specific to ET-VK. Remove the existing `FuseDequantLinearPass()` Switch to using the `FuseQuantizedOpsTransform` pass instead of the old `FuseDequantLinear` pass. Add `test_vulkan_passes` Python test to test export passes. Some small refactors to `test_vulkan_delegate` Python test to improve code organizations. Differential Revision: [D73794042](https://our.internmc.facebook.com/intern/diff/D73794042/) [ghstack-poisoned]
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## Context When quantizing models with the PT2E quantization flow, quantize/dequantize nodes will be inserted into the graph. However, these quantize/dequantize nodes must be fused with operators such as `aten.linear.default` to produce nodes corresponding to quantized operators (e.g. `weight_int8pack_mm`) in order for quantized operator implementations to be called at runtime. Currently, the op fusion is done by the `fuse_dequant_linear.py` pass, however, this only handles one specific fusion pattern to generate a `weight_int8pack_mm` operator. As more quantized operators are to be supported in ET-VK via the PT2E quantization flow, a more generic fusion pass is needed that can handle a variety of fusion patterns. ## Changes Introduce the `FuseQuantizedOpsTransform()` pass. I elected to introduce a new pass under the `backends/vulkan/_passes` directory, as opposed to modifying the existing pass because I anticipate the majority of the fusion patterns to be specific to ET-VK. Remove the existing `FuseDequantLinearPass()` Switch to using the `FuseQuantizedOpsTransform` pass instead of the old `FuseDequantLinear` pass. Add `test_vulkan_passes` Python test to test export passes. Some small refactors to `test_vulkan_delegate` Python test to improve code organizations. Differential Revision: [D73794042](https://our.internmc.facebook.com/intern/diff/D73794042/) [ghstack-poisoned]
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Pull Request resolved: #10525 ## Context When quantizing models with the PT2E quantization flow, quantize/dequantize nodes will be inserted into the graph. However, these quantize/dequantize nodes must be fused with operators such as `aten.linear.default` to produce nodes corresponding to quantized operators (e.g. `weight_int8pack_mm`) in order for quantized operator implementations to be called at runtime. Currently, the op fusion is done by the `fuse_dequant_linear.py` pass, however, this only handles one specific fusion pattern to generate a `weight_int8pack_mm` operator. As more quantized operators are to be supported in ET-VK via the PT2E quantization flow, a more generic fusion pass is needed that can handle a variety of fusion patterns. ## Changes Introduce the `FuseQuantizedOpsTransform()` pass. I elected to introduce a new pass under the `backends/vulkan/_passes` directory, as opposed to modifying the existing pass because I anticipate the majority of the fusion patterns to be specific to ET-VK. Remove the existing `FuseDequantLinearPass()` Switch to using the `FuseQuantizedOpsTransform` pass instead of the old `FuseDequantLinear` pass. Add `test_vulkan_passes` Python test to test export passes. Added some refactors to `test_vulkan_delegate` Python test to improve code organization. Introduce the `linear_qcsnw` nomenclature: * q - quantized * c - per-channel / channelswise * s - symmetric * n - number of bits (qcs4w for 4-bit quant, qcs8w for 8-bit quant) * w - weight quantized Added custom op for `linear_qcs4w` for 4-bit weight quantized linear and add the ability for the quantized op fusion pass to produce this op. Slight renaming/refactoring of quantization config retrieval functions in the `VulkanQuantizer` to improve clarity and API flexibility. ghstack-source-id: 281448174 @exported-using-ghexport Differential Revision: [D73794042](https://our.internmc.facebook.com/intern/diff/D73794042/)
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## Context When quantizing models with the PT2E quantization flow, quantize/dequantize nodes will be inserted into the graph. However, these quantize/dequantize nodes must be fused with operators such as `aten.linear.default` to produce nodes corresponding to quantized operators (e.g. `weight_int8pack_mm`) in order for quantized operator implementations to be called at runtime. Currently, the op fusion is done by the `fuse_dequant_linear.py` pass, however, this only handles one specific fusion pattern to generate a `weight_int8pack_mm` operator. As more quantized operators are to be supported in ET-VK via the PT2E quantization flow, a more generic fusion pass is needed that can handle a variety of fusion patterns. ## Changes Introduce the `FuseQuantizedOpsTransform()` pass. I elected to introduce a new pass under the `backends/vulkan/_passes` directory, as opposed to modifying the existing pass because I anticipate the majority of the fusion patterns to be specific to ET-VK. Remove the existing `FuseDequantLinearPass()` Switch to using the `FuseQuantizedOpsTransform` pass instead of the old `FuseDequantLinear` pass. Add `test_vulkan_passes` Python test to test export passes. Some small refactors to `test_vulkan_delegate` Python test to improve code organizations. Differential Revision: [D73794042](https://our.internmc.facebook.com/intern/diff/D73794042/) [ghstack-poisoned]
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## Context When quantizing models with the PT2E quantization flow, quantize/dequantize nodes will be inserted into the graph. However, these quantize/dequantize nodes must be fused with operators such as `aten.linear.default` to produce nodes corresponding to quantized operators (e.g. `weight_int8pack_mm`) in order for quantized operator implementations to be called at runtime. Currently, the op fusion is done by the `fuse_dequant_linear.py` pass, however, this only handles one specific fusion pattern to generate a `weight_int8pack_mm` operator. As more quantized operators are to be supported in ET-VK via the PT2E quantization flow, a more generic fusion pass is needed that can handle a variety of fusion patterns. ## Changes Introduce the `FuseQuantizedOpsTransform()` pass. I elected to introduce a new pass under the `backends/vulkan/_passes` directory, as opposed to modifying the existing pass because I anticipate the majority of the fusion patterns to be specific to ET-VK. Remove the existing `FuseDequantLinearPass()` Switch to using the `FuseQuantizedOpsTransform` pass instead of the old `FuseDequantLinear` pass. Add `test_vulkan_passes` Python test to test export passes. Some small refactors to `test_vulkan_delegate` Python test to improve code organizations. Differential Revision: [D73794042](https://our.internmc.facebook.com/intern/diff/D73794042/) [ghstack-poisoned]
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@kimishpatel apologies, only just saw your comment. I'm using I suppose the proper thing to do is to register the implementation under an equivalent custom op (i.e. Btw, another factor for why |
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Stack from ghstack (oldest at bottom):
Context
When quantizing models with the PT2E quantization flow, quantize/dequantize nodes will be inserted into the graph. However, these quantize/dequantize nodes must be fused with operators such as
aten.linear.defaultto produce nodes corresponding to quantized operators (e.g.weight_int8pack_mm) in order for quantized operator implementations to be called at runtime.Currently, the op fusion is done by the
fuse_dequant_linear.pypass, however, this only handles one specific fusion pattern to generate aweight_int8pack_mmoperator. As more quantized operators are to be supported in ET-VK via the PT2E quantization flow, a more generic fusion pass is needed that can handle a variety of fusion patterns.Changes
Introduce the
FuseQuantizedOpsTransform()pass. I elected to introduce a new pass under thebackends/vulkan/_passesdirectory, as opposed to modifying the existing pass because I anticipate the majority of the fusion patterns to be specific to ET-VK.Remove the existing
FuseDequantLinearPass()Switch to using the
FuseQuantizedOpsTransformpass instead of the oldFuseDequantLinearpass.Add
test_vulkan_passesPython test to test export passes.Some small refactors to
test_vulkan_delegatePython test to improve code organizations.Differential Revision: D73794042