[quant] Implement PTQ for APoT FakeQuant #81040
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### Summary: This PR implements FX Graph Mode QAT for APoT FakeQuant. ### Test Plan: Run models with: `python test/quantization/core/experimental/fx_graph_mode_apot.py` Accuracy Stats: Model #1: uniform activation, uniform weight (FX Graph Mode quantized) Size of model (MB): 46.801265 Evaluation accuracy on test dataset: 69.76%, 89.08% Model #2: uniform activation, APoT weight (FX Graph Mode quantized) Size of model (MB): 46.820369 Evaluation accuracy on test dataset: 69.00%, 88.66% Model #3: APoT activation and weight (FX Graph Mode quantized) Size of model (MB): 46.801431 Evaluation accuracy on test dataset: 69.76%, 89.08% Eager mode quantized model Resnet18 Size of model (MB): 11.839989 Evaluation accuracy on test dataset: 69.49%, 88.90% [ghstack-poisoned]
### Summary: This PR implements FX Graph Mode QAT for APoT FakeQuant. ### Test Plan: Run models with: `python test/quantization/core/experimental/fx_graph_mode_apot.py` ### Accuracy Stats: Uniform: int8 APoT: 8-bit (b = 8, k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 69.54%, 88.99% **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 69.76%, 89.08% **Model #3:** APoT activation and weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 69.54%, 89.04% **Model #4:** Eager mode quantized model Resnet18 Evaluation accuracy on test dataset: 69.49%, 88.90% [ghstack-poisoned]
### Summary: This PR implements FX Graph Mode QAT for APoT FakeQuant. ### Test Plan: Run models with: `python test/quantization/core/experimental/fx_graph_mode_apot.py` ### Accuracy Stats: Uniform: int8 APoT: 8-bit (b = 8, k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 69.54%, 88.99% **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 69.76%, 89.08% **Model #3:** APoT activation and weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 69.54%, 89.04% **Model #4:** Eager mode quantized model Resnet18 Evaluation accuracy on test dataset: 69.49%, 88.90% [ghstack-poisoned]
### Summary: This PR implements FX Graph Mode QAT for APoT FakeQuant. ### Test Plan: Run models with: `python test/quantization/core/experimental/fx_graph_mode_apot.py` ### Accuracy Stats: Uniform: int8 APoT: 8-bit (b = 8, k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 69.54%, 88.99% **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 69.76%, 89.08% **Model #3:** APoT activation and weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 69.54%, 89.04% **Model #4:** Eager mode quantized model Resnet18 Evaluation accuracy on test dataset: 69.49%, 88.90% [ghstack-poisoned]
### Summary: This PR implements FX Graph Mode QAT for APoT FakeQuant. ### Test Plan: Run models with: `python test/quantization/core/experimental/fx_graph_mode_apot.py` ### Accuracy Stats: Uniform: int8 APoT: 8-bit (b = 8, k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 69.54%, 88.99% **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 69.76%, 89.08% **Model #3:** APoT activation and weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 69.54%, 89.04% **Model #4:** Eager mode quantized model Resnet18 Evaluation accuracy on test dataset: 69.49%, 88.90% [ghstack-poisoned]
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this PR is for PTQ and not QAT? |
asl3
changed the title
### Summary: This PR implements FX Graph Mode QAT for APoT FakeQuant. ### Test Plan: Run models with: `python test/quantization/core/experimental/fx_graph_mode_apot.py` ### Accuracy Stats: Uniform: int8 APoT: 8-bit (b = 8, k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 69.54%, 88.99% **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 69.76%, 89.08% **Model #3:** APoT activation and weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 69.54%, 89.04% **Model #4:** Eager mode quantized model Resnet18 Evaluation accuracy on test dataset: 69.49%, 88.90% [ghstack-poisoned]
jerryzh168
reviewed
Jul 26, 2022
jerryzh168
reviewed
Jul 26, 2022
jerryzh168
reviewed
Jul 26, 2022
jerryzh168
reviewed
Jul 26, 2022
jerryzh168
reviewed
Jul 26, 2022
jerryzh168
reviewed
Jul 26, 2022
jerryzh168
reviewed
Jul 26, 2022
### Summary: This PR implements PTQ for APoT FakeQuant. ### Test Plan: Run models with: `python test/quantization/core/experimental/fx_graph_mode_apot.py` ### Accuracy Stats: 8-bit (Uniform int8, APoT b = 8 k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.43%, 85.62% **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.51%, 85.78% **Model #3:** APoT activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.32%, 85.78% 4-bit (Uniform int4, APoT b = 8 k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 45.63%, 71.96% **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.24%, 85.56% **Model #3:** APoT activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 45.40%, 76.21% **Full Precision model (FX Graph Mode quantized)** Evaluation accuracy on test dataset: 69.76%, 89.08% **Eager mode quantized model** Evaluation accuracy on test dataset: 69.49%, 88.90% [ghstack-poisoned]
### Summary: This PR implements PTQ for APoT FakeQuant. ### Test Plan: Run models with: `python test/quantization/core/experimental/fx_graph_mode_apot.py` ### Accuracy Stats: 8-bit (Uniform int8, APoT b = 8 k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.43%, 85.62% **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.51%, 85.78% **Model #3:** APoT activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.32%, 85.78% 4-bit (Uniform int4, APoT b = 8 k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 45.63%, 71.96% **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.24%, 85.56% **Model #3:** APoT activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 45.40%, 76.21% **Full Precision model (FX Graph Mode quantized)** Evaluation accuracy on test dataset: 69.76%, 89.08% **Eager mode quantized model** Evaluation accuracy on test dataset: 69.49%, 88.90% [ghstack-poisoned]
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### Summary: This PR implements PTQ for APoT FakeQuant. It runs models (Resnet-18 pre-trained model, ImageNet dataset) to compare accuracy metrics for different qconfig settings of uniform vs. APoT quantized activation and weight. According to the collected accuracy stats, model #2 (uniform activation and APoT weight) appears to have a slight improvement in accuracy compared to model #1 (uniform activation and uniform weight) for 8-bit and significant improvement for 4-bit (see "Accuracy Stats" section below). ### Test Plan: Run models with: `python test/quantization/core/experimental/fx_graph_mode_apot.py` ### Accuracy Stats: 8-bit (Uniform int8, APoT b = 8 k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.43% (Top-1), 85.62% (Top-5) **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.51% (Top-1), 85.78% (Top-5) **Model #3:** APoT activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.32% (Top-1), 85.78% (Top-5) 4-bit (Uniform int4, APoT b = 4 k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 45.63% (Top-1), 71.96% (Top-5) **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.24% (Top-1), 85.56% (Top-5) **Model #3:** APoT activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 45.40% (Top-1), 76.21% (Top-5) **Full Precision model (FX Graph Mode quantized)** Evaluation accuracy on test dataset: 69.76% (Top-1), 89.08% (Top-5) **Eager mode quantized model** Evaluation accuracy on test dataset: 69.49% (Top-1), 88.90% (Top-5) [ghstack-poisoned]
### Summary: This PR implements PTQ for APoT FakeQuant. It runs models (Resnet-18 pre-trained model, ImageNet dataset) to compare accuracy metrics for different qconfig settings of uniform vs. APoT quantized activation and weight. According to the collected accuracy stats, model #2 (uniform activation and APoT weight) appears to have a slight improvement in accuracy compared to model #1 (uniform activation and uniform weight) for 8-bit and significant improvement for 4-bit (see "Accuracy Stats" section below). ### Test Plan: Run models with: `python test/quantization/core/experimental/fx_graph_mode_apot.py` ### Accuracy Stats: 8-bit (Uniform int8, APoT b = 8 k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.43% (Top-1), 85.62% (Top-5) **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.51% (Top-1), 85.78% (Top-5) **Model #3:** APoT activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.32% (Top-1), 85.78% (Top-5) 4-bit (Uniform int4, APoT b = 4 k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 45.63% (Top-1), 71.96% (Top-5) **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.24% (Top-1), 85.56% (Top-5) **Model #3:** APoT activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 45.40% (Top-1), 76.21% (Top-5) **Full Precision model (FX Graph Mode quantized)** Evaluation accuracy on test dataset: 69.76% (Top-1), 89.08% (Top-5) **Eager mode quantized model** Evaluation accuracy on test dataset: 69.49% (Top-1), 88.90% (Top-5) [ghstack-poisoned]
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### Summary: This PR implements PTQ for APoT FakeQuant. It runs models (Resnet-18 pre-trained model, ImageNet dataset) to compare accuracy metrics for different qconfig settings of uniform vs. APoT quantized activation and weight. According to the collected accuracy stats, model #2 (uniform activation and APoT weight) appears to have a slight improvement in accuracy compared to model #1 (uniform activation and uniform weight) for 8-bit and significant improvement for 4-bit (see "Accuracy Stats" section below). ### Test Plan: Run models with: `python test/quantization/core/experimental/fx_graph_mode_apot.py` ### Accuracy Stats: 8-bit (Uniform int8, APoT b = 8 k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.43% (Top-1), 85.62% (Top-5) **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.51% (Top-1), 85.78% (Top-5) **Model #3:** APoT activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.32% (Top-1), 85.78% (Top-5) 4-bit (Uniform int4, APoT b = 4 k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 45.63% (Top-1), 71.96% (Top-5) **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.24% (Top-1), 85.56% (Top-5) **Model #3:** APoT activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 45.40% (Top-1), 76.21% (Top-5) **Full Precision model (FX Graph Mode quantized)** Evaluation accuracy on test dataset: 69.76% (Top-1), 89.08% (Top-5) **Eager mode quantized model** Evaluation accuracy on test dataset: 69.49% (Top-1), 88.90% (Top-5) [ghstack-poisoned]
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### Summary: This PR implements PTQ for APoT FakeQuant. It runs models (Resnet-18 pre-trained model, ImageNet dataset) to compare accuracy metrics for different qconfig settings of uniform vs. APoT quantized activation and weight. According to the collected accuracy stats, model #2 (uniform activation and APoT weight) appears to have a slight improvement in accuracy compared to model #1 (uniform activation and uniform weight) for 8-bit and significant improvement for 4-bit (see "Accuracy Stats" section below). ### Test Plan: Run models with: `python test/quantization/core/experimental/fx_graph_mode_apot.py` ### Accuracy Stats: 8-bit (Uniform int8, APoT b = 8 k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.43% (Top-1), 85.62% (Top-5) **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.51% (Top-1), 85.78% (Top-5) **Model #3:** APoT activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.32% (Top-1), 85.78% (Top-5) 4-bit (Uniform int4, APoT b = 4 k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 45.63% (Top-1), 71.96% (Top-5) **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.24% (Top-1), 85.56% (Top-5) **Model #3:** APoT activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 45.40% (Top-1), 76.21% (Top-5) **Full Precision model (FX Graph Mode quantized)** Evaluation accuracy on test dataset: 69.76% (Top-1), 89.08% (Top-5) **Eager mode quantized model** Evaluation accuracy on test dataset: 69.49% (Top-1), 88.90% (Top-5) [ghstack-poisoned]
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Summary: ### Summary: This PR implements PTQ for APoT FakeQuant. It runs models (Resnet-18 pre-trained model, ImageNet dataset) to compare accuracy metrics for different qconfig settings of uniform vs. APoT quantized activation and weight. According to the collected accuracy stats, model #2 (uniform activation and APoT weight) appears to have a slight improvement in accuracy compared to model #1 (uniform activation and uniform weight) for 8-bit and significant improvement for 4-bit (see "Accuracy Stats" section below). ### Test Plan: Run models with: `python test/quantization/core/experimental/fx_graph_mode_apot.py` ### Accuracy Stats: 8-bit (Uniform int8, APoT b = 8 k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.43% (Top-1), 85.62% (Top-5) **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.51% (Top-1), 85.78% (Top-5) **Model #3:** APoT activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.32% (Top-1), 85.78% (Top-5) 4-bit (Uniform int4, APoT b = 4 k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 45.63% (Top-1), 71.96% (Top-5) **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.24% (Top-1), 85.56% (Top-5) **Model #3:** APoT activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 45.40% (Top-1), 76.21% (Top-5) **Full Precision model (FX Graph Mode quantized)** Evaluation accuracy on test dataset: 69.76% (Top-1), 89.08% (Top-5) **Eager mode quantized model** Evaluation accuracy on test dataset: 69.49% (Top-1), 88.90% (Top-5) Pull Request resolved: #81040 Approved by: https://github.com/jerryzh168 Test Plan: contbuild & OSS CI, see https://hud.pytorch.org/commit/pytorch/pytorch/13ad4739a6e9402e2039a1ce521b9aed595760b3 Reviewed By: osalpekar Differential Revision: D38252390 Pulled By: asl3 fbshipit-source-id: 86ff2f3928fb1fc2b57867d6abcac998d17306e4
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Summary:
This PR implements PTQ for APoT FakeQuant. It runs models (Resnet-18 pre-trained model, ImageNet dataset) to compare accuracy metrics for different qconfig settings of uniform vs. APoT quantized activation and weight.
According to the collected accuracy stats, model #2 (uniform activation and APoT weight) appears to have a slight improvement in accuracy compared to model #1 (uniform activation and uniform weight) for 8-bit and significant improvement for 4-bit (see "Accuracy Stats" section below).
Test Plan:
Run models with:
python test/quantization/core/experimental/fx_graph_mode_apot.pyAccuracy Stats:
8-bit (Uniform int8, APoT b = 8 k = 2)
Model #1: Uniform activation, uniform weight (FX Graph Mode quantized)
Evaluation accuracy on test dataset: 64.43% (Top-1), 85.62% (Top-5)
Model #2: Uniform activation, APoT weight (FX Graph Mode quantized)
Evaluation accuracy on test dataset: 64.51% (Top-1), 85.78% (Top-5)
Model #3: APoT activation, APoT weight (FX Graph Mode quantized)
Evaluation accuracy on test dataset: 64.32% (Top-1), 85.78% (Top-5)
4-bit (Uniform int4, APoT b = 4 k = 2)
Model #1: Uniform activation, uniform weight (FX Graph Mode quantized)
Evaluation accuracy on test dataset: 45.63% (Top-1), 71.96% (Top-5)
Model #2: Uniform activation, APoT weight (FX Graph Mode quantized)
Evaluation accuracy on test dataset: 64.24% (Top-1), 85.56% (Top-5)
Model #3: APoT activation, APoT weight (FX Graph Mode quantized)
Evaluation accuracy on test dataset: 45.40% (Top-1), 76.21% (Top-5)
Full Precision model (FX Graph Mode quantized)
Evaluation accuracy on test dataset: 69.76% (Top-1), 89.08% (Top-5)
Eager mode quantized model
Evaluation accuracy on test dataset: 69.49% (Top-1), 88.90% (Top-5)