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Mixtral fix: match reference with standalone script #2054

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Jan 28, 2025
Merged

Mixtral fix: match reference with standalone script #2054

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87 changes: 16 additions & 71 deletions language/mixtral-8x7b/SUT.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,11 +30,12 @@
log = logging.getLogger("Mixtral-8x7B-Instruct-v0.1")

gen_kwargs = {
"early_stopping": True,
"max_new_tokens": 1024,
# "min_new_tokens": 1,
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"min_new_tokens": 2,
"num_beams": 1,
"max_new_tokens": 1024,
"do_sample": False,
"temperature": None,
"top_p": None,
}


Expand Down Expand Up @@ -238,80 +239,30 @@ def process_queries(self):
input_masks_tensor = []
input_len = []
input_dataset = []
batch_texts = []
datasets = []
for q in qitem:
input_ids_tensor.append(
pad(
self.data_object.input_ids[q.index],
(
max_seq_len -
self.data_object.input_lens[q.index],
0,
0,
0,
),
value=self.tokenizer.pad_token_id,
)
)
input_masks_tensor.append(
pad(
self.data_object.attention_masks[q.index],
(
max_seq_len -
self.data_object.input_lens[q.index],
0,
0,
0,
),
value=0,
)
)
batch_texts.append(self.data_object.input_texts[q.index])
input_len.append(self.data_object.input_lens[q.index])

# In case we predict code generation, we can specify an
# additional stop sequence
input_dataset.append(
self.data_object.dataset_names[q.index])
input_ids_tensor = torch.cat(input_ids_tensor)
input_masks_tensor = torch.cat(input_masks_tensor)

assert input_ids_tensor.shape == input_masks_tensor.shape
assert input_ids_tensor.shape[0] <= self.batch_size
batch_ids = self.tokenizer.batch_encode_plus(batch_texts, return_tensors="pt", padding=True)
batch_ids = batch_ids.to(self.device)

tik2 = time.time()
logits_processor = LogitsProcessorList(
[StopAfterSequence(
self.tokenizer.eos_token_id, device=self.device)]
)
for i in range(len(input_ids_tensor)):
ids, masks, dataset = (
input_ids_tensor[i: i + 1],
input_masks_tensor[i: i + 1],
input_dataset[i],
)
pred_output_tokens = []
if dataset == "MBXP":
out = self.model.generate(
input_ids=ids,
attention_mask=masks,
pad_token_id=self.tokenizer.pad_token_id,
logits_processor=logits_processor,
**gen_kwargs,
)
else:
out = self.model.generate(
input_ids=ids,
attention_mask=masks,
pad_token_id=self.tokenizer.pad_token_id,
**gen_kwargs,
)
pred_output_tokens.append(out)
pred_output_tokens = torch.cat(pred_output_tokens)
_, length = batch_ids.input_ids.shape
out = self.model.generate(**batch_ids, num_return_sequences=1, **gen_kwargs)
pred_output_tokens = out
tik3 = time.time()

processed_output = self.data_object.postProcess(
pred_output_tokens,
input_seq_lens=input_len,
length=length,
query_id_list=query_ids,
dataset_list=input_dataset,
)

for i in range(len(qitem)):
Expand Down Expand Up @@ -342,10 +293,7 @@ def process_queries(self):

def load_model(self):
self.model = AutoModelForCausalLM.from_pretrained(
self.model_path,
device_map="auto",
low_cpu_mem_usage=True,
torch_dtype=self.amp_dtype,
self.model_path, device_map="auto", trust_remote_code=True
)
print("Loaded model")

Expand All @@ -362,10 +310,7 @@ def load_model(self):
pass

self.tokenizer = AutoTokenizer.from_pretrained(
self.model_path,
model_max_length=1024,
padding_side="left",
use_fast=False,
self.model_path, padding_side="left", trust_remote_code=True
)

self.tokenizer.pad_token = self.tokenizer.eos_token
Expand Down
29 changes: 25 additions & 4 deletions language/mixtral-8x7b/dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,7 @@ def load_processed_dataset(self):
processed_data = pd.read_pickle(self.dataset_path)

input_tokens = processed_data["tok_input"]
self.input_texts = processed_data["input"].to_list()

self.input_ids = []
self.input_lens = []
Expand All @@ -85,12 +86,31 @@ def load_processed_dataset(self):
self.dataset_names.append(dataset)
print("Finished loading dataset.")


def remove_trailing_twos(self, lst, eos = 2):
count = 0
for num in reversed(lst):
if num == eos or num == 0:
count += 1
else:
break
return lst[:-count] if count > 0 else lst


def mbxp_stop(self, lst, stop_tokens = [13, 13940, 28832, 13]):
for i in range(len(lst) - len(stop_tokens) + 1):
if (lst[i:i+len(stop_tokens)] == stop_tokens).all():
return lst[:i+len(stop_tokens)]
return lst


def postProcess(
self,
out_tokens,
input_seq_lens=None,
length=None,
query_id_list=None,
sample_index_list=None,
dataset_list=None,
):
"""Postprocesses output prediction"""

Expand All @@ -106,13 +126,14 @@ def postProcess(
"""
# Everything is padded to max_len (1024), so prune the input and parse
# to numpy
output_seq = out_tokens[:, 1024:].cpu().numpy()
output_seq = out_tokens[:, length:].cpu().numpy()
aux_seq = []
assert len(query_id_list) == output_seq.shape[0]
for i in range(len(output_seq)):
aux = output_seq[i]
while len(output_seq[i]) <= 1:
aux = np.append(aux, self.tokenizer.eos_token_id)
aux = self.remove_trailing_twos(aux)
if (dataset_list[i] == "MBXP"):
aux = self.mbxp_stop(aux)
aux_seq.append(aux)
output_seq = np.stack(aux_seq)

Expand Down
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