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chore(style): reduce line length and truncate compression

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Signed-off-by: aarnphm-ec2-dev <29749331+aarnphm@users.noreply.github.com>
This commit is contained in:
aarnphm-ec2-dev
2023-08-22 17:02:00 +00:00
parent bc851b1d13
commit eddbc06374
67 changed files with 1661 additions and 301 deletions
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31
openllm-python/src/openllm/_quantisation.py
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@@ -24,14 +24,27 @@ def infer_quantisation_config(cls: type[LLM[t.Any, t.Any]], quantise: QuantiseMo
int8_skip_modules: list[str] | None = attrs.pop("llm_int8_skip_modules", None)
int8_has_fp16_weight = attrs.pop("llm_int8_has_fp16_weight", False)
autogptq_attrs: DictStrAny = {"bits": attrs.pop("gptq_bits", 4), "group_size": attrs.pop("gptq_group_size", -1), "damp_percent": attrs.pop("gptq_damp_percent", 0.01), "desc_act": attrs.pop("gptq_desc_act", True), "sym": attrs.pop("gptq_sym", True), "true_sequential": attrs.pop("gptq_true_sequential", True),}
autogptq_attrs: DictStrAny = {
"bits": attrs.pop("gptq_bits", 4),
"group_size": attrs.pop("gptq_group_size", -1),
"damp_percent": attrs.pop("gptq_damp_percent", 0.01),
"desc_act": attrs.pop("gptq_desc_act", True),
"sym": attrs.pop("gptq_sym", True),
"true_sequential": attrs.pop("gptq_true_sequential", True),
}
def create_int8_config(int8_skip_modules: list[str] | None) -> transformers.BitsAndBytesConfig:
if int8_skip_modules is None: int8_skip_modules = []
if "lm_head" not in int8_skip_modules and cls.config_class.__openllm_model_type__ == "causal_lm":
logger.debug("Skipping 'lm_head' for quantization for %s", cls.__name__)
int8_skip_modules.append("lm_head")
return transformers.BitsAndBytesConfig(load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=int8_enable_fp32_cpu_offload, llm_int8_threshhold=int8_threshold, llm_int8_skip_modules=int8_skip_modules, llm_int8_has_fp16_weight=int8_has_fp16_weight,)
return transformers.BitsAndBytesConfig(
load_in_8bit=True,
llm_int8_enable_fp32_cpu_offload=int8_enable_fp32_cpu_offload,
llm_int8_threshhold=int8_threshold,
llm_int8_skip_modules=int8_skip_modules,
llm_int8_has_fp16_weight=int8_has_fp16_weight,
)
# 4 bit configuration
int4_compute_dtype = attrs.pop("bnb_4bit_compute_dtype", torch.bfloat16)
@@ -44,13 +57,21 @@ def infer_quantisation_config(cls: type[LLM[t.Any, t.Any]], quantise: QuantiseMo
if not is_bitsandbytes_available(): raise RuntimeError("Quantization requires bitsandbytes to be installed. Make sure to install OpenLLM with 'pip install \"openllm[fine-tune]\"'")
if quantise == "int8": quantisation_config = create_int8_config(int8_skip_modules)
elif quantise == "int4":
if is_transformers_supports_kbit(): quantisation_config = transformers.BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=int4_compute_dtype, bnb_4bit_quant_type=int4_quant_type, bnb_4bit_use_double_quant=int4_use_double_quant)
if is_transformers_supports_kbit():
quantisation_config = transformers.BitsAndBytesConfig(
load_in_4bit=True, bnb_4bit_compute_dtype=int4_compute_dtype, bnb_4bit_quant_type=int4_quant_type, bnb_4bit_use_double_quant=int4_use_double_quant
)
else:
logger.warning("'quantize' is set to int4, while the current transformers version %s does not support k-bit quantization. k-bit quantization is supported since transformers 4.30, therefore make sure to install the latest version of transformers either via PyPI or from git source: 'pip install git+https://github.com/huggingface/transformers'. Fallback to int8 quantisation.", pkg.pkg_version_info("transformers"))
logger.warning(
"'quantize' is set to int4, while the current transformers version %s does not support k-bit quantization. k-bit quantization is supported since transformers 4.30, therefore make sure to install the latest version of transformers either via PyPI or from git source: 'pip install git+https://github.com/huggingface/transformers'. Fallback to int8 quantisation.",
pkg.pkg_version_info("transformers")
)
quantisation_config = create_int8_config(int8_skip_modules)
elif quantise == "gptq":
if not is_autogptq_available():
logger.warning("'quantize=\"gptq\"' requires 'auto-gptq' to be installed (not available with local environment). Make sure to have 'auto-gptq' available locally: 'pip install \"openllm[gptq]\"'. OpenLLM will fallback to int8 with bitsandbytes.")
logger.warning(
"'quantize=\"gptq\"' requires 'auto-gptq' to be installed (not available with local environment). Make sure to have 'auto-gptq' available locally: 'pip install \"openllm[gptq]\"'. OpenLLM will fallback to int8 with bitsandbytes."
)
quantisation_config = create_int8_config(int8_skip_modules)
else:
quantisation_config = autogptq.BaseQuantizeConfig(**autogptq_attrs)