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fix: enabling block-by-block evaluation for granite-3.x-models #165

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Merged
chichun-charlie-liu merged 3 commits into from
Jul 24, 2025
Merged

fix: enabling block-by-block evaluation for granite-3.x-models #165

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bf53238
fix: Enabled eval_llm_1GPU for granite-3.x-models
bayo-ibm Jul 22, 2025
6c1da4c
fix: updated ways of getting obtaining logits_scaling
bayo-ibm Jul 23, 2025
3d5b342
fix: added comments to the logits scaling calculation in eval_llm_1GPU
bayo-ibm Jul 24, 2025
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10 changes: 9 additions & 1 deletion fms_mo/quant/ptq.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2140,14 +2140,22 @@ def get_blocks(model, model_type=None):
None,
"lm_head",
),
"granite": (
"granite_gptbigcode": (
"transformer.h",
"transformer.wte",
"transformer.wpe",
None,
"transformer.ln_f",
"lm_head",
),
"granite": (
"model.layers",
"model.embed_tokens",
"model.rotary_emb",
None,
"model.norm",
"lm_head",
),
"llama": (
"model.layers",
"model.embed_tokens",
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1 change: 0 additions & 1 deletion fms_mo/run_quant.py
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Expand Up @@ -155,7 +155,6 @@ def run_gptq(model_args, data_args, opt_args, gptq_args):
v2_memory_device="cpu",
)


# Add custom model_type mapping to gptqmodel LUT so GPTQModel can recognize them.
for mtype, cls in custom_gptq_classes.items():
if mtype in MODEL_MAP:
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5 changes: 2 additions & 3 deletions fms_mo/training_args.py
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Expand Up @@ -139,8 +139,8 @@ class OptArguments(TypeChecker):

quant_method: str = field(
metadata={
"choices": ["gptq", "gptqv2", "fp8", "dq"],
"help": "Quantization technique"
"choices": ["gptq", "gptqv2", "fp8", "dq"],
"help": "Quantization technique",
}
)
output_dir: str = field(
Expand Down Expand Up @@ -229,7 +229,6 @@ class GPTQArguments(TypeChecker):
cache_examples_on_gpu: bool = True



@dataclass
class FP8Arguments(TypeChecker):
"""Dataclass for FP8 related arguments that will be used by llm-compressor."""
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3 changes: 3 additions & 0 deletions fms_mo/utils/eval_utils.py
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Expand Up @@ -107,6 +107,9 @@ def eval_llm_1GPU(qcfg, model, test_dataset, pre_cache_func=None, **kwargs): #
lm_head.to(dev)
lm_logits = lm_head(hidden_states)

if model.config.model_type == "granite":
lm_logits /= model.config.logits_scaling

# Shift so that tokens < n predict n
shift_logits = lm_logits[:, :-1, :].contiguous().float()
shift_labels = test_dataset.input_ids[:, (i * seq_len) : ((i + 1) * seq_len)][
Expand Down
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