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Author SHA1 Message Date
Wing Lian
1a22d16842 handle empty offset for quant state 2025-05-01 13:01:00 -04:00
5 changed files with 15 additions and 40 deletions

View File

@@ -4,12 +4,6 @@ on:
pull_request:
types: [opened, synchronize, reopened]
# Run the workflow only when one of these files changes
paths:
- '**/*.md' # any Markdown file
- '**/*.qmd' # any Quarto file
- '_quarto.yaml'
permissions:
checks: write
contents: write

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@@ -177,8 +177,12 @@ class AxolotlDPOTrainer(RngLoaderMixin, SchedulerMixin, DPOTrainer):
# dpo trainer may incorrectly prepend the bos_token_id to the dpo outputs
if res["chosen_input_ids"][0] == processing_class.bos_token_id:
res["chosen_input_ids"] = res["chosen_input_ids"][1:]
res["chosen_labels"] = res["chosen_labels"][1:]
res["chosen_attention_mask"] = res["chosen_attention_mask"][1:]
if res["rejected_input_ids"][0] == processing_class.bos_token_id:
res["rejected_input_ids"] = res["rejected_input_ids"][1:]
res["rejected_labels"] = res["rejected_labels"][1:]
res["rejected_attention_mask"] = res["rejected_attention_mask"][1:]
return res

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@@ -55,13 +55,16 @@ def dequantize(
target_device = W.device
# Extract quantization state
nested = False
if not isinstance(quant_state, list):
# New style quant_state class
absmax = quant_state.absmax.to(target_device)
shape = quant_state.shape
dtype = quant_state.dtype
blocksize = quant_state.blocksize
offset = quant_state.offset.to(target_device)
if quant_state.nested:
nested = True
offset = quant_state.offset.to(target_device)
state2 = quant_state.state2
absmax2 = state2.absmax.to(target_device)
code2 = state2.code.to(target_device)
@@ -115,7 +118,8 @@ def dequantize(
ctypes.c_int(n_elements_absmax),
)
out_absmax += offset
if nested:
out_absmax += offset
# Choose appropriate dequantization function
fx = (

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@@ -512,17 +512,10 @@ class AxolotlInputConfig(
@model_validator(mode="before")
@classmethod
def hint_sample_packing_padding(cls, data):
if data.get("sample_packing"):
pad_to_sequence_len = data.get("pad_to_sequence_len")
if pad_to_sequence_len is False:
LOG.warning(
"`pad_to_sequence_len: true` is recommended when using sample_packing"
)
elif pad_to_sequence_len is None:
LOG.info(
"Setting `pad_to_sequence_len: true` to prevent memory leaks when sample_packing"
)
data["pad_to_sequence_len"] = True
if data.get("sample_packing") and not data.get("pad_to_sequence_len"):
LOG.warning(
"`pad_to_sequence_len: true` is recommended when using sample_packing"
)
return data
@model_validator(mode="before")

View File

@@ -648,7 +648,7 @@ class TestValidation(BaseValidation):
DictDefault(
{
"sample_packing": True,
"pad_to_sequence_len": False,
"pad_to_sequence_len": None,
"flash_attention": True,
}
)
@@ -662,26 +662,6 @@ class TestValidation(BaseValidation):
for record in self._caplog.records
)
def test_packing_autoset(self, minimal_cfg):
cfg = (
DictDefault(
{
"sample_packing": True,
"pad_to_sequence_len": None,
"flash_attention": True,
}
)
| minimal_cfg
)
with self._caplog.at_level(logging.INFO):
cfg = validate_config(cfg)
assert any(
"Setting `pad_to_sequence_len: true` to prevent memory leaks when sample_packing"
in record.message
for record in self._caplog.records
)
assert cfg.pad_to_sequence_len is True
def test_merge_lora_no_bf16_fail(self, minimal_cfg):
"""
This is assumed to be run on a CPU machine, so bf16 is not supported.