mirror of
https://github.com/ggml-org/llama.cpp.git
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56b4795842
* model-conversion : add support for SentenceTransformers This commit adds support for models that use SentenceTransformer layers. The motivation for this is that if converted model includes any of the numbered layers specified in the original models repository then these changes enable these models to be used and verified. Currently the model-conversion only support the base model output without any of the additional transformation layers. Usage: Convert the model that also includes the SentenceTransformer layers: ```console (venv) $ export EMBEDDING_MODEL_PATH="~/google/embeddinggemma-300M" (venv) make embedding-convert-model ``` Verify the produced embeddings from the converted model against the original model embeddings: ```console (venv) make embedding-verify-logits-st ``` The original model can be run using SentenceTransformer: ```console (venv) make embedding-run-original-model-st ``` Run the converted model using "SentenceTransformer" layers whic enables pooling and normalization: ```console (venv) make embedding-run-converted-model-st ``` * add model-conversion example requirements * add support for -st flag in embedding model conversion This commit add support for the -st flag in the embedding model conversion script. This will enable models to be converted using sentence transformers dense layers.
231 lines
8.5 KiB
Makefile
231 lines
8.5 KiB
Makefile
MAKEFLAGS += --no-print-directory
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define validate_model_path
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@if [ -z "$(MODEL_PATH)" ]; then \
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echo "Error: MODEL_PATH must be provided either as:"; \
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echo " 1. Environment variable: export MODEL_PATH=/path/to/model"; \
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echo " 2. Command line argument: make $(1) MODEL_PATH=/path/to/model"; \
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exit 1; \
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fi
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endef
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define validate_embedding_model_path
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@if [ -z "$(EMBEDDING_MODEL_PATH)" ]; then \
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echo "Error: EMBEDDING_MODEL_PATH must be provided either as:"; \
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echo " 1. Environment variable: export EMBEDDING_MODEL_PATH=/path/to/model"; \
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echo " 2. Command line argument: make $(1) EMBEDDING_MODEL_PATH=/path/to/model"; \
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exit 1; \
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fi
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endef
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define quantize_model
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@CONVERTED_MODEL="$(1)" QUANTIZED_TYPE="$(QUANTIZED_TYPE)" \
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TOKEN_EMBD_TYPE="$(TOKEN_EMBD_TYPE)" OUTPUT_TYPE="$(OUTPUT_TYPE)" \
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./scripts/utils/quantize.sh "$(1)" "$(QUANTIZED_TYPE)" "$(TOKEN_EMBD_TYPE)" "$(OUTPUT_TYPE)"
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@echo "Export the quantized model path to $(2) variable in your environment"
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endef
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###
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### Casual Model targets/recipes
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###
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causal-convert-model-bf16: OUTTYPE=bf16
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causal-convert-model-bf16: causal-convert-model
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causal-convert-model:
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$(call validate_model_path,causal-convert-model)
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@MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(OUTTYPE)" MODEL_PATH="$(MODEL_PATH)" \
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METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
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./scripts/causal/convert-model.sh
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causal-convert-mm-model-bf16: OUTTYPE=bf16
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causal-convert-mm-model-bf16: MM_OUTTYPE=f16
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causal-convert-mm-model-bf16: causal-convert-mm-model
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causal-convert-mm-model:
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$(call validate_model_path,causal-convert-mm-model)
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@MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(OUTTYPE)" MODEL_PATH="$(MODEL_PATH)" \
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METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
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./scripts/causal/convert-model.sh
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@MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(MM_OUTTYPE)" MODEL_PATH="$(MODEL_PATH)" \
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METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
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./scripts/causal/convert-model.sh --mmproj
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causal-run-original-model:
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$(call validate_model_path,causal-run-original-model)
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@MODEL_PATH="$(MODEL_PATH)" ./scripts/causal/run-org-model.py
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causal-run-converted-model:
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@CONVERTED_MODEL="$(CONVERTED_MODEL)" ./scripts/causal/run-converted-model.sh
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causal-verify-logits: causal-run-original-model causal-run-converted-model
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@./scripts/causal/compare-logits.py
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@MODEL_PATH="$(MODEL_PATH)" ./scripts/utils/check-nmse.py -m ${MODEL_PATH}
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causal-run-original-embeddings:
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@./scripts/causal/run-casual-gen-embeddings-org.py
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causal-run-converted-embeddings:
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@./scripts/causal/run-converted-model-embeddings-logits.sh
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causal-verify-embeddings: causal-run-original-embeddings causal-run-converted-embeddings
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@./scripts/causal/compare-embeddings-logits.sh
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causal-inspect-original-model:
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@./scripts/utils/inspect-org-model.py
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causal-inspect-converted-model:
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@./scripts/utils/inspect-converted-model.sh
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causal-start-embedding-server:
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@./scripts/utils/run-embedding-server.sh ${CONVERTED_MODEL}
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causal-curl-embedding-endpoint: causal-run-original-embeddings
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@./scripts/utils/curl-embedding-server.sh | ./scripts/causal/compare-embeddings-logits.sh
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causal-quantize-Q8_0: QUANTIZED_TYPE = Q8_0
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causal-quantize-Q8_0: causal-quantize-model
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causal-quantize-Q4_0: QUANTIZED_TYPE = Q4_0
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causal-quantize-Q4_0: causal-quantize-model
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# For Quantization Aware Trained (QAT) models in Q4_0 we explicitly set the
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# token embedding and output types to Q8_0 instead of the default Q6_K.
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causal-quantize-qat-Q4_0: QUANTIZED_TYPE = Q4_0
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causal-quantize-qat-Q4_0: TOKEN_EMBD_TYPE = Q8_0
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causal-quantize-qat-Q4_0: OUTPUT_TYPE = Q8_0
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causal-quantize-qat-Q4_0: causal-quantize-model
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causal-quantize-model:
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$(call quantize_model,$(CONVERTED_MODEL),QUANTIZED_MODEL)
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causal-run-quantized-model:
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@QUANTIZED_MODEL="$(QUANTIZED_MODEL)" ./scripts/causal/run-converted-model.sh ${QUANTIZED_MODEL}
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###
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### Embedding Model targets/recipes
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###
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embedding-convert-model-bf16: OUTTYPE=bf16
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embedding-convert-model-bf16: embedding-convert-model
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embedding-convert-model:
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$(call validate_embedding_model_path,embedding-convert-model)
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@MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(OUTTYPE)" MODEL_PATH="$(EMBEDDING_MODEL_PATH)" \
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METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
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./scripts/embedding/convert-model.sh
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embedding-convert-model-st:
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$(call validate_embedding_model_path,embedding-convert-model-st)
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@MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(OUTTYPE)" MODEL_PATH="$(EMBEDDING_MODEL_PATH)" \
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METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
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./scripts/embedding/convert-model.sh -st
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embedding-run-original-model:
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$(call validate_embedding_model_path,embedding-run-original-model)
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@EMBEDDING_MODEL_PATH="$(EMBEDDING_MODEL_PATH)" \
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USE_SENTENCE_TRANSFORMERS="$(USE_SENTENCE_TRANSFORMERS)" \
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./scripts/embedding/run-original-model.py \
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$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)") \
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$(if $(USE_SENTENCE_TRANSFORMERS),--use-sentence-transformers)
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embedding-run-original-model-st: USE_SENTENCE_TRANSFORMERS=1
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embedding-run-original-model-st: embedding-run-original-model
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embedding-run-converted-model:
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@./scripts/embedding/run-converted-model.sh $(CONVERTED_EMBEDDING_MODEL) \
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$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)") \
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$(if $(USE_POOLING),--pooling)
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embedding-run-converted-model-st: USE_POOLING=1
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embedding-run-converted-model-st: embedding-run-converted-model
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embedding-verify-logits: embedding-run-original-model embedding-run-converted-model
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@./scripts/embedding/compare-embeddings-logits.sh \
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$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)")
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embedding-verify-logits-st: embedding-run-original-model-st embedding-run-converted-model-st
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@./scripts/embedding/compare-embeddings-logits.sh \
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$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)")
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embedding-inspect-original-model:
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$(call validate_embedding_model_path,embedding-inspect-original-model)
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@EMBEDDING_MODEL_PATH="$(EMBEDDING_MODEL_PATH)" ./scripts/utils/inspect-org-model.py -m ${EMBEDDING_MODEL_PATH}
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embedding-inspect-converted-model:
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@CONVERTED_EMBEDDING_MODEL="$(CONVERTED_EMBEDDING_MODEL)" ./scripts/utils/inspect-converted-model.sh ${CONVERTED_EMBEDDING_MODEL}
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embedding-start-embedding-server:
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@./scripts/utils/run-embedding-server.sh ${CONVERTED_EMBEDDING_MODEL}
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embedding-curl-embedding-endpoint:
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@./scripts/utils/curl-embedding-server.sh | ./scripts/embedding/compare-embeddings-logits.sh
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embedding-quantize-Q8_0: QUANTIZED_TYPE = Q8_0
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embedding-quantize-Q8_0: embedding-quantize-model
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embedding-quantize-Q4_0: QUANTIZED_TYPE = Q4_0
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embedding-quantize-Q4_0: embedding-quantize-model
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# For Quantization Aware Trained (QAT) models in Q4_0 we explicitly set the
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# token embedding and output types to Q8_0 instead of the default Q6_K.
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embedding-quantize-qat-Q4_0: QUANTIZED_TYPE = Q4_0
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embedding-quantize-qat-Q4_0: TOKEN_EMBD_TYPE = Q8_0
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embedding-quantize-qat-Q4_0: OUTPUT_TYPE = Q8_0
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embedding-quantize-qat-Q4_0: embedding-quantize-model
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embedding-quantize-model:
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$(call quantize_model,$(CONVERTED_EMBEDDING_MODEL),QUANTIZED_EMBEDDING_MODEL)
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embedding-run-quantized-model:
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@./scripts/embedding/run-converted-model.sh $(QUANTIZED_EMBEDDING_MODEL) \
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$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)")
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###
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### Perplexity targets/recipes
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###
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perplexity-data-gen:
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CONVERTED_MODEL="$(CONVERTED_MODEL)" ./scripts/utils/perplexity-gen.sh
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perplexity-run-full:
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QUANTIZED_MODEL="$(QUANTIZED_MODEL)" LOOGITS_FILE="$(LOGITS_FILE)" \
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./scripts/utils/perplexity-run.sh
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perplexity-run:
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QUANTIZED_MODEL="$(QUANTIZED_MODEL)" ./scripts/utils/perplexity-run-simple.sh
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###
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### HuggingFace targets/recipes
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###
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hf-create-model:
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@./scripts/utils/hf-create-model.py -m "${MODEL_NAME}" -ns "${NAMESPACE}" -b "${ORIGINAL_BASE_MODEL}"
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hf-create-model-dry-run:
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@./scripts/utils/hf-create-model.py -m "${MODEL_NAME}" -ns "${NAMESPACE}" -b "${ORIGINAL_BASE_MODEL}" -d
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hf-create-model-embedding:
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@./scripts/utils/hf-create-model.py -m "${MODEL_NAME}" -ns "${NAMESPACE}" -b "${ORIGINAL_BASE_MODEL}" -e
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hf-create-model-embedding-dry-run:
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@./scripts/utils/hf-create-model.py -m "${MODEL_NAME}" -ns "${NAMESPACE}" -b "${ORIGINAL_BASE_MODEL}" -e -d
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hf-create-model-private:
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@./scripts/utils/hf-create-model.py -m "${MODEL_NAME}" -ns "${NAMESPACE}" -b "${ORIGINAL_BASE_MODEL}" -p
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hf-upload-gguf-to-model:
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@./scripts/utils/hf-upload-gguf-model.py -m "${MODEL_PATH}" -r "${REPO_ID}" -o "${NAME_IN_REPO}"
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hf-create-collection:
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@./scripts/utils/hf-create-collection.py -n "${NAME}" -d "${DESCRIPTION}" -ns "${NAMESPACE}"
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hf-add-model-to-collection:
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@./scripts/utils/hf-add-model-to-collection.py -c "${COLLECTION}" -m "${MODEL}"
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.PHONY: clean
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clean:
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@${RM} -rf data .converted_embedding_model.txt .converted_model.txt .embedding_model_name.txt .model_name.txt
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