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Configuration error
Configuration error
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import time
from tools.schema import (
ServeStreamDelta,
ServeStreamResponse,
ServeTextPart,
ServeVQPart,
)
def initialize_decode_buffers(num_samples):
"""Initialise the decode buffers for each sample."""
decode_buffer = [[] for _ in range(num_samples)]
parts = [[] for _ in range(num_samples)]
finished = [False for _ in range(num_samples)]
return decode_buffer, parts, finished
def send_reset_buffer(sample_id, decode_buffer, tokenizer, parts, request):
"""Send the remaining text buffer for a sample."""
if len(decode_buffer[sample_id]) == 0:
return []
decoded = tokenizer.decode(decode_buffer[sample_id])
part = ServeTextPart(text=decoded)
responses = []
if request.streaming:
responses.append(ServeStreamResponse(delta=ServeStreamDelta(part=part)))
else:
parts[sample_id].append(part)
decode_buffer[sample_id] = []
return responses
def handle_semantic_tokens(tokens, config, sample_id, parts, request):
"""Handle the semantic tokens returned by the model."""
responses = []
_tokens = tokens[1:].clone()
if not config.share_codebook_embeddings:
for i in range(len(_tokens)):
_tokens[i] -= config.codebook_size * i
# If streaming, send the VQ parts directly
if request.streaming:
responses.append(
ServeStreamResponse(
sample_id=sample_id,
delta=ServeStreamDelta(part=ServeVQPart(codes=_tokens.tolist())),
)
)
else:
# If not streaming, accumulate the VQ parts
if not parts[sample_id] or not isinstance(parts[sample_id][-1], ServeVQPart):
parts[sample_id].append(ServeVQPart(codes=_tokens.tolist()))
else:
# Accumulate the codes
for codebook_id, value in enumerate(_tokens):
parts[sample_id][-1].codes[codebook_id].append(value.item())
return responses
def process_response_tokens(
response,
tokenizer,
config,
request,
decode_buffer,
parts,
finished,
im_end_id,
stats,
start,
is_first_token,
):
"""Process the response tokens returned by the model."""
responses = []
for sample_id, tokens in enumerate(response):
if finished[sample_id]:
continue
# End of the conversation
if tokens[0] == im_end_id:
finished[sample_id] = True
# Send the remaining text buffer
responses.extend(
send_reset_buffer(sample_id, decode_buffer, tokenizer, parts, request)
)
if request.streaming:
responses.append(
ServeStreamResponse(
sample_id=sample_id,
finish_reason="stop",
stats=stats,
)
)
continue
# Check if the token is semantic
is_semantic = (
tokenizer.semantic_begin_id <= tokens[0] <= tokenizer.semantic_end_id
)
if is_semantic:
# Before the semantic tokens, send the remaining text buffer
responses.extend(
send_reset_buffer(sample_id, decode_buffer, tokenizer, parts, request)
)
responses.extend(
handle_semantic_tokens(tokens, config, sample_id, parts, request)
)
else:
# Accumulate the text tokens (not implemented?)
decode_buffer[sample_id].append(tokens[0, 0])
if is_first_token:
stats["time_to_first_token"] = (time.time() - start) * 1000
return responses
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