huggingface/text-generation-inference
5
0
Fork 0
mirror of https://github.com/huggingface/text-generation-inference.git synced 2025-05-21 17:52:09 +00:00
Code Issues Packages Projects Releases Wiki Activity

Prefill optimization by allocating space only for the first output token (#34) (#62)

Browse Source 
Co-authored-by: mswiniarsk <156412439+mswiniarsk@users.noreply.github.com>
Co-authored-by: Karol Damaszke <karol.damaszke@intel.com>
This commit is contained in:
jkaniecki 2024-02-22 04:55:43 +01:00 committed by GitHub
parent 80303b469c
commit 8f590759e3
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
2 changed files with 57 additions and 32 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

85
server/text_generation_server/models/causal_lm.py
View File

@ -140,6 +140,15 @@ def remove_kv_cache_from_output(module):
return module
def pad_tensors(tensors, paddings, dim, value):
for i, (tensor, padding) in enumerate(zip(tensors, paddings)):
if padding > 0:
pad_shape = (0, 0, 0, padding) if dim == -2 else (0, padding)
tensors[i] = torch.nn.functional.pad(tensor, pad_shape, value=value)
htorch.core.mark_step()
return tensors
@dataclass
class CausalLMRequest:
idx: int
@ -196,7 +205,7 @@ class CausalLMBatch(Batch):
)
@classmethod
def recombine(cls, batches: List["CausalLMBatch"], is_optimized_for_gaudi: bool = False) -> "CausalLMBatch":
def recombine(cls, batches: List["CausalLMBatch"], pad_token_id: int) -> "CausalLMBatch":
total_requests = sum(len(b) for b in batches)
new_bs = round_up(total_requests, BATCH_BUCKET_SIZE)
batch_id = batches[0].batch_id
@ -224,7 +233,8 @@ class CausalLMBatch(Batch):
return batches[0]
inplace = batches[target_batch_idx].batch_size == new_bs
dbg_trace(scenario, f'bs:{[b.batch_size for b in batches]}->{new_bs} reqs:{[len(b) for b in batches]} offsets:{offsets} padding:{padding} moves_needed:{moves_needed} inplace:{inplace}')
dbg_trace(
scenario, f'bs:{[b.batch_size for b in batches]}->{new_bs} reqs:{[len(b) for b in batches]} offsets:{offsets} padding:{padding} moves_needed:{moves_needed} inplace:{inplace}')
grouped_requests = [[req for req in batch.requests] for batch in batches]
flat_requests = list(itertools.chain(*grouped_requests))
@ -235,12 +245,9 @@ class CausalLMBatch(Batch):
else:
free_indices = itertools.count(0)
to_tensors = lambda ind: (torch.tensor(ind[0], device=device), torch.tensor(ind[1], device=device))
indices = [[to_tensors(req.update_idx(next(free_indices))) for req in batch_reqs] for batch_reqs in grouped_requests]
max_seq_len = batches[0].attention_mask.size(1)
input_length = max_input_length
right_padding = max_seq_len - input_length
def to_tensors(ind): return (torch.tensor(ind[0], device=device), torch.tensor(ind[1], device=device))
indices = [[to_tensors(req.update_idx(next(free_indices))) for req in batch_reqs]
for batch_reqs in grouped_requests]
chunk_size = batches[0].past_key_values[0][0].size(0) // batches[0].batch_size
num_layers = len(batches[0].past_key_values)
@ -257,9 +264,14 @@ class CausalLMBatch(Batch):
for b in batches:
b.past_key_values = list(b.past_key_values)
# For prefill there is a space allocated only for first token
# Need to add padding to the max total tokens before first decode
paddings = [(batch.input_length + batch.right_padding) - batch.seq_length for batch in batches]
src = [b.input_ids for b in batches]
for b in batches:
del b.input_ids
src = pad_tensors(src, paddings, seq_dim, pad_token_id)
src = shift_all(src, seq_dim, offsets)
input_ids = prepare_memory(new_bs, src[target_batch_idx], inplace)
input_ids = move_data(input_ids, 1, indices, src)
@ -267,6 +279,7 @@ class CausalLMBatch(Batch):
src = [b.attention_mask for b in batches]
for b in batches:
del b.attention_mask
src = pad_tensors(src, paddings, seq_dim, 0)
src = shift_all(src, seq_dim, offsets)
attention_mask = prepare_memory(new_bs, src[target_batch_idx], inplace)
attention_mask = move_data(attention_mask, 1, indices, src)
@ -281,11 +294,13 @@ class CausalLMBatch(Batch):
past_key_values = []
for layer_num in range(num_layers):
src = [b.past_key_values[layer_num][0] for b in batches]
src = pad_tensors(src, paddings, key_dim, 0)
src = shift_all(src, key_dim, offsets)
updated_key = prepare_memory(new_bs * chunk_size, src[target_batch_idx], inplace)
updated_key = move_data(updated_key, chunk_size, indices, src)
src = [b.past_key_values[layer_num][1] for b in batches]
src = pad_tensors(src, paddings, value_dim, 0)
src = shift_all(src, value_dim, offsets)
updated_value = prepare_memory(new_bs * chunk_size, src[target_batch_idx], inplace)
updated_value = move_data(updated_value, chunk_size, indices, src)
@ -304,6 +319,10 @@ class CausalLMBatch(Batch):
batches[0].next_token_chooser.device
)
max_seq_len = attention_mask.size(1)
input_length = max_input_length
right_padding = max_seq_len - input_length
htorch.core.mark_step()
return cls(
@ -320,7 +339,6 @@ class CausalLMBatch(Batch):
right_padding=right_padding
)
@classmethod
def from_pb(
cls,
@ -378,12 +396,16 @@ class CausalLMBatch(Batch):
attention_mask = tokenized_inputs["attention_mask"]
if is_optimized_for_gaudi:
# Allocate space for first token
input_ids = torch.nn.functional.pad(
input_ids, (0, max_new_tokens + extra_padding), value=tokenizer.pad_token_id
input_ids, (0, 1), value=tokenizer.pad_token_id
)
attention_mask = torch.nn.functional.pad(
attention_mask, (0, max_new_tokens + extra_padding), value=0)
all_input_ids = input_ids.T.split(1, dim=1)
attention_mask, (0, 1), value=0
)
all_input_ids = torch.nn.functional.pad(
input_ids, (0, max_new_tokens + extra_padding - 1), value=tokenizer.pad_token_id
).T.split(1, dim=1)
else:
all_input_ids = input_ids.clone().T.split(1, dim=1)
@ -412,7 +434,7 @@ class CausalLMBatch(Batch):
)
@tracer.start_as_current_span("filter")
def filter(self, request_ids: List[int], is_optimized_for_gaudi: bool = False) -> Optional["CausalLMBatch"]:
def filter(self, request_ids: List[int]) -> Optional["CausalLMBatch"]:
dbg_trace('FILTER', f'num_reqs:{len(self.requests)} -> {len(request_ids)}')
request_ids = set(request_ids)
self.requests = [req for req in self.requests if req.data.id in request_ids]
@ -420,8 +442,8 @@ class CausalLMBatch(Batch):
@classmethod
@tracer.start_as_current_span("concatenate")
def concatenate(cls, batches: List["CausalLMBatch"], is_optimized_for_gaudi: bool = False) -> "CausalLMBatch":
return cls.recombine(batches, is_optimized_for_gaudi)
def concatenate(cls, batches: List["CausalLMBatch"], pad_token_id: int = 0) -> "CausalLMBatch":
return cls.recombine(batches, pad_token_id)
def __len__(self):
return len(self.requests)
@ -517,7 +539,6 @@ class CausalLM(Model):
ds_inference_kwargs["tensor_parallel"] = {"tp_size": world_size}
ds_inference_kwargs["enable_cuda_graph"] = False
if load_to_meta:
# model loaded to meta is managed differently
checkpoints_json = tempfile.NamedTemporaryFile(suffix=".json", mode="+w")
@ -537,7 +558,7 @@ class CausalLM(Model):
torch_dtype=dtype,
)
model = model.eval().to(device)
#wrap in hpu_graph only if self.enable_hpu_graph is set
# wrap in hpu_graph only if self.enable_hpu_graph is set
model = remove_kv_cache_from_output(model)
if self.enable_hpu_graph:
model = wrap_in_hpu_graph(model, disable_tensor_cache=True)
@ -608,7 +629,6 @@ class CausalLM(Model):
else:
return super().decode_token(all_input_ids, prefix_offset, read_offset)
def forward(
self,
input_ids,
@ -646,10 +666,11 @@ class CausalLM(Model):
prefill = batch.past_key_values is None
# Check if we need to do any bookkeeping first
if not prefill:
batch = batch.__class__.recombine([batch], self.is_optimized_for_gaudi)
batch = batch.__class__.recombine([batch], self.tokenizer.pad_token_id)
scenario = 'PREFILL' if prefill else 'GENERATE'
dbg_trace(scenario, f'bs:{batch.batch_size} num_reqs:{len(batch.requests)} seq_len:{batch.seq_length} padding:{batch.right_padding}')
dbg_trace(
scenario, f'bs:{batch.batch_size} num_reqs:{len(batch.requests)} seq_len:{batch.seq_length} padding:{batch.right_padding}')
assert batch.right_padding > 0, 'No more room for next token!'
self.step = self.step + 1
if self.hb_profer_started == True and self.step > self.profiling_warmup_steps + self.profiling_steps:
@ -657,16 +678,20 @@ class CausalLM(Model):
self.hb_profer_started = False
if self.is_optimized_for_gaudi:
token_idx = torch.tensor(batch.attention_mask.shape[-1] - batch.right_padding).to(self.device)
if prefill:
# no right padding for prefill
token_idx = torch.tensor(batch.attention_mask.shape[-1] - 1).to(self.device)
else:
token_idx = torch.tensor(batch.attention_mask.shape[-1] - batch.right_padding).to(self.device)
attention_mask = batch.attention_mask
else:
token_idx = None
# slice the attention mask to the correct shape
# TODO fix me!
attention_mask = batch.attention_mask[:, : -batch.padding_right_offset]
if batch.past_key_values:
if token_idx is not None:
input_ids = torch.index_select(batch.input_ids, 1, token_idx - 1)
if not prefill and token_idx is not None:
input_ids = torch.index_select(batch.input_ids, 1, token_idx - 1)
else:
input_ids = batch.input_ids
@ -677,7 +702,7 @@ class CausalLM(Model):
batch.position_ids,
token_idx,
batch.past_key_values,
bypass_hpu_graph = prefill and self.limit_hpu_graph if self.enable_hpu_graph else None
bypass_hpu_graph=prefill and self.limit_hpu_graph if self.enable_hpu_graph else None
)
else:
logits = self.forward(
@ -686,7 +711,7 @@ class CausalLM(Model):
batch.position_ids,
token_idx,
batch.past_key_values,
bypass_hpu_graph = prefill and self.limit_hpu_graph if self.enable_hpu_graph else None
bypass_hpu_graph=prefill and self.limit_hpu_graph if self.enable_hpu_graph else None
)
# Results
@ -697,7 +722,7 @@ class CausalLM(Model):
input_length = batch.input_length
if self.is_optimized_for_gaudi and logits.shape[-2] > 1:
next_token_ids, next_token_logprobs, logprobs = batch.next_token_chooser(
batch.input_ids[:, :token_idx], logits[:, input_length - 1 : input_length, :].squeeze(-2)
batch.input_ids[:, :token_idx], logits[:, input_length - 1: input_length, :].squeeze(-2)
)
else:
next_token_ids, next_token_logprobs, logprobs = batch.next_token_chooser(
@ -757,7 +782,7 @@ class CausalLM(Model):
if stop:
# Decode generated tokens
output_text = self.decode(
all_input_ids[new_input_length - stopping_criteria.current_tokens : new_input_length, 0]
all_input_ids[new_input_length - stopping_criteria.current_tokens: new_input_length, 0]
)
generated_text = GeneratedText(
output_text,
@ -772,7 +797,7 @@ class CausalLM(Model):
if stopping_criteria.current_tokens == 1 and request.prefill_logprobs:
# Remove generated token to only have prefill and add nan for first prompt token
prefill_logprobs = [float("nan")] + next_token_logprobs
prefill_token_ids = all_input_ids[0 : new_input_length - 1]
prefill_token_ids = all_input_ids[0: new_input_length - 1]
prefill_texts = self.tokenizer.batch_decode(
prefill_token_ids,
clean_up_tokenization_spaces=False,
@ -846,7 +871,7 @@ class CausalLM(Model):
# Update position_ids
if prefill:
batch.position_ids = batch.position_ids[:, token_idx - 1 : token_idx] + 1
batch.position_ids = batch.position_ids[:, token_idx - 1: token_idx] + 1
else:
batch.position_ids += 1
# Update past key values

4
server/text_generation_server/server.py
View File

@ -59,7 +59,7 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
{"util": len(batch.requests)}):
if batch is None:
raise ValueError(f"Batch ID {request.batch_id} not found in cache.")
filtered_batch = batch.filter(request.request_ids, self.model.is_optimized_for_gaudi)
filtered_batch = batch.filter(request.request_ids, self.model.tokenizer.pad_token_id)
self.cache.set(filtered_batch)
return generate_pb2.FilterBatchResponse(batch=filtered_batch.to_pb())
@ -113,7 +113,7 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
if len(batches) > 1:
with self.profiler.record_event("internal", "concatenate"):
batch = self.model.batch_type.concatenate(batches, self.model.is_optimized_for_gaudi)
batch = self.model.batch_type.concatenate(batches, self.model.tokenizer.pad_token_id)
else:
batch = batches[0]