8000 Incorporate embedding pooling layer fixes by iamlemec · Pull Request #1194 · abetlen/llama-cpp-python · GitHub
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Incorporate embedding pooling layer fixes #1194

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Merged
merged 2 commits into from
Feb 15, 2024
Merged

Incorporate embedding pooling layer fixes #1194

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ee84ca1
remove division by token count
iamlemec Feb 15, 2024
fa7f1cd
truncate to n_batch, not n_ctx
iamlemec Feb 15, 2024
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33 changes: 17 additions & 16 deletions llama_cpp/llama.py
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Original file line number Diff line number Diff line change
Expand Up @@ -762,7 +762,7 @@ def embed(
"""
assert self._ctx.ctx is not None
n_embd = self.n_embd()
n_ctx = self.n_ctx()
n_batch = self.n_batch

if self.context_params.embedding == False:
raise RuntimeError(
Expand All @@ -782,54 +782,55 @@ def embed(

# decode and fetch embeddings
data: List[List[float]] = []
def decode_batch(sizes: List[int]):
def decode_batch(n_seq: int):
assert self._ctx.ctx is not None
llama_cpp.llama_kv_cache_clear(self._ctx.ctx)
self._ctx.decode(self._batch)
self._batch.reset()

# store embeddings
for i, s in enumerate(sizes):
embedding = llama_cpp.llama_get_embeddings_ith(self._ctx.ctx, i)[
for i in range(n_seq):
embedding: List[float] = llama_cpp.llama_get_embeddings_ith(self._ctx.ctx, i)[
:n_embd
]
norm = np.linalg.norm(embedding) if normalize else s
embedding: List[float] = [v / float(norm) for v in embedding]
if normalize:
norm = float(np.linalg.norm(embedding))
embedding = [v / norm for v in embedding]
data.append(embedding)

# init state
total_tokens = 0
t_batch = 0
s_sizes: List[int] = []
p_batch = 0

# accumulate batches and encode
for text in inputs:
tokens = self.tokenize(text.encode("utf-8"))
if truncate:
tokens = tokens[:n_ctx]
tokens = tokens[:n_batch]

n_tokens = len(tokens)
total_tokens += n_tokens

# check for overrun
if n_tokens > n_ctx:
if n_tokens > n_batch:
raise ValueError(
f"Requested tokens ({n_tokens}) exceed context window of {n_ctx}"
f"Requested tokens ({n_tokens}) exceed batch size of {n_batch}"
)

# time to eval batch
if t_batch + n_tokens > self._n_ctx:
decode_batch(s_sizes)
if t_batch + n_tokens > n_batch:
decode_batch(p_batch)
t_batch = 0
s_sizes = []
p_batch = 0

# add to batch
self._batch.add_sequence(tokens, len(s_sizes), False)
self._batch.add_sequence(tokens, p_batch, False)
t_batch += n_tokens
s_sizes.append(n_tokens)
p_batch += 1

# hanlde last batch
decode_batch(s_sizes)
decode_batch(p_batch)

if self.verbose:
llama_cpp.llama_print_timings(self._ctx.ctx)
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