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model-conversion : add qat-q4 quantization targets (#15588)

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This commit adds two targets to the Makefile for quantizing of
Quantization Aware Trained (QAT) models to Q4_0 format.

The motivation for this is that this sets the token embedding and the
output tensors data types to Q8_0 instead of the default Q6_K. This is
someting that we wish to enforce for QAT Q4_0 models that are to be
uploaded to ggml-org on Huggingface to guarantee the best quality.
This commit is contained in:
Daniel Bevenius
2025-08-26 16:12:29 +02:00
committed by GitHub
parent 8f5afa94c4
commit 62cef26ac5
3 changed files with 65 additions and 7 deletions
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30
examples/model-conversion/Makefile
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@@ -1,4 +1,5 @@
# Validation functions
MAKEFLAGS += --no-print-directory
define validate_model_path
@if [ -z "$(MODEL_PATH)" ]; then \
echo "Error: MODEL_PATH must be provided either as:"; \
@@ -17,6 +18,13 @@ define validate_embedding_model_path
fi
endef
define quantize_model
@CONVERTED_MODEL="$(1)" QUANTIZED_TYPE="$(QUANTIZED_TYPE)" \
TOKEN_EMBD_TYPE="$(TOKEN_EMBD_TYPE)" OUTPUT_TYPE="$(OUTPUT_TYPE)" \
./scripts/utils/quantize.sh "$(1)" "$(QUANTIZED_TYPE)" "$(TOKEN_EMBD_TYPE)" "$(OUTPUT_TYPE)"
@echo "Export the quantized model path to $(2) variable in your environment"
endef
###
### Casual Model targets/recipes
###
@@ -67,9 +75,15 @@ causal-quantize-Q8_0: causal-quantize-model
causal-quantize-Q4_0: QUANTIZED_TYPE = Q4_0
causal-quantize-Q4_0: causal-quantize-model
# For Quantization Aware Trained (QAT) models in Q4_0 we explicitly set the
# token embedding and output types to Q8_0 instead of the default Q6_K.
causal-quantize-qat-Q4_0: QUANTIZED_TYPE = Q4_0
causal-quantize-qat-Q4_0: TOKEN_EMBD_TYPE = Q8_0
causal-quantize-qat-Q4_0: OUTPUT_TYPE = Q8_0
causal-quantize-qat-Q4_0: causal-quantize-model
causal-quantize-model:
@CONVERTED_MODEL="$(CONVERTED_MODEL)" QUANTIZED_TYPE="$(QUANTIZED_TYPE)" ./scripts/utils/quantize.sh ${CONVERTED_MODEL} ${QUANTIZED_TYPE}
@echo "Export the quantized model path to QUANTIZED_MODEL variable in your environment"
$(call quantize_model,$(CONVERTED_MODEL),QUANTIZED_MODEL)
causal-run-quantized-model:
@QUANTIZED_MODEL="$(QUANTIZED_MODEL)" ./scripts/causal/run-converted-model.sh ${QUANTIZED_MODEL}
@@ -117,9 +131,15 @@ embedding-quantize-Q8_0: embedding-quantize-model
embedding-quantize-Q4_0: QUANTIZED_TYPE = Q4_0
embedding-quantize-Q4_0: embedding-quantize-model
# For Quantization Aware Trained (QAT) models in Q4_0 we explicitly set the
# token embedding and output types to Q8_0 instead of the default Q6_K.
embedding-quantize-qat-Q4_0: QUANTIZED_TYPE = Q4_0
embedding-quantize-qat-Q4_0: TOKEN_EMBD_TYPE = Q8_0
embedding-quantize-qat-Q4_0: OUTPUT_TYPE = Q8_0
embedding-quantize-qat-Q4_0: embedding-quantize-model
embedding-quantize-model:
@./scripts/utils/quantize.sh ${CONVERTED_EMBEDDING_MODEL} ${QUANTIZED_TYPE}
@echo "Export the quantized model path to QUANTIZED_EMBEDDING_MODEL variable in your environment"
$(call quantize_model,$(CONVERTED_EMBEDDING_MODEL),QUANTIZED_EMBEDDING_MODEL)
embedding-run-quantized-model:
@./scripts/embedding/run-converted-model.sh ${QUANTIZED_EMBEDDING_MODEL}

24
examples/model-conversion/README.md
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@@ -137,6 +137,18 @@ Then the quantized model can be run using the following command:
(venv) $ make causal-run-quantized-model
```
### Quantizing QAT (Quantization Aware Training) models
When quantizing to `Q4_0`, the default data type for the token embedding weights
will be `Q6_K`. For models that are going to be uploaded to ggml-org it is
recommended to use `Q8_0` instead for the embeddings and output tensors.
The reason is that although `Q6_K` is smaller in size, it requires more compute
to unpack, which can hurt performance during output generation when the entire
embedding matrix must be dequantized to compute vocabulary logits. `Q8_0`
provides practically full quality with better computational efficiency.
```console
(venv) $ make causal-quantize-qat-Q4_0
```
## Embedding Language Model Conversion
@@ -238,6 +250,18 @@ Then the quantized model can be run using the following command:
(venv) $ make embedding-run-quantized-model
```
### Quantizing QAT (Quantization Aware Training) models
When quantizing to `Q4_0`, the default data type for the token embedding weights
will be `Q6_K`. For models that are going to be uploaded to ggml-org it is
recommended to use `Q8_0` instead for the embeddings and output tensors.
The reason is that although `Q6_K` is smaller in size, it requires more compute
to unpack, which can hurt performance during output generation when the entire
embedding matrix must be dequantized to compute vocabulary logits. `Q8_0`
provides practically full quality with better computational efficiency.
```console
(venv) $ make embedding-quantize-qat-Q4_0
```
## Perplexity Evaluation
### Simple perplexity evaluation

18
examples/model-conversion/scripts/utils/quantize.sh
View File

@@ -4,6 +4,8 @@ set -e
CONVERTED_MODEL="${1:-"$CONVERTED_MODEL"}"
QUANTIZED_TYPE="${2:-"$QUANTIZED_TYPE"}"
TOKEN_EMBD_TYPE="${3:-"${TOKEN_EMBD_TYPE}"}"
OUTPUT_TYPE="${4:-"${OUTPUT_TYPE}"}"
QUANTIZED_MODEL=$CONVERTED_MODEL
# Final check if we have a model path
@@ -14,6 +16,11 @@ if [ -z "$CONVERTED_MODEL" ]; then
exit 1
fi
if [ -z "$QUANTIZED_TYPE" ]; then
echo "Error: QUANTIZED_TYPE is required" >&2
exit 1
fi
echo $CONVERTED_MODEL
# Process the quantized model filename
@@ -26,9 +33,16 @@ else
exit 1
fi
cmake --build ../../build --target llama-quantize -j8
../../build/bin/llama-quantize $CONVERTED_MODEL $QUANTIZED_MODEL $QUANTIZED_TYPE
echo $TOKEN_EMBD_TYPE
echo $OUTPUT_TYPE
CMD_ARGS=("../../build/bin/llama-quantize")
[[ -n "$TOKEN_EMBD_TYPE" ]] && CMD_ARGS+=("--token-embedding-type" "$TOKEN_EMBD_TYPE")
[[ -n "$OUTPUT_TYPE" ]] && CMD_ARGS+=("--output-tensor-type" "$OUTPUT_TYPE")
CMD_ARGS+=("$CONVERTED_MODEL" "$QUANTIZED_MODEL" "$QUANTIZED_TYPE")
"${CMD_ARGS[@]}"
echo "Quantized model saved to: $QUANTIZED_MODEL"