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__init__ | super().__init__()
self.gate_up_proj = MergedColumnParallelLinear(hidden_size, [
intermediate_size] * 2, bias=False, linear_method=linear_method)
self.c_proj = RowParallelLinear(intermediate_size, hidden_size, bias=False,
linear_method=linear_method)
if hidden_act != 'silu':
raise ValueError(
f'Unsupported activation: {hidden_act}. Only silu is supported for now.'
)
self.act_fn = SiluAndMul() | def __init__(self, hidden_size: int, intermediate_size: int, hidden_act:
str='silu', linear_method: Optional[LinearMethodBase]=None):
super().__init__()
self.gate_up_proj = MergedColumnParallelLinear(hidden_size, [
intermediate_size] * 2, bias=False, linear_method=linear_method)
self.c_proj = RowParallelLinear(intermediate_size, hidden_size, bias=
False, linear_method=linear_method)
if hidden_act != 'silu':
raise ValueError(
f'Unsupported activation: {hidden_act}. Only silu is supported for now.'
)
self.act_fn = SiluAndMul() | null |
__init__ | super().__init__()
self.hidden_size = hidden_size
tp_size = get_tensor_model_parallel_world_size()
self.total_num_heads = num_heads
assert self.total_num_heads % tp_size == 0
self.num_heads = self.total_num_heads // tp_size
self.total_num_kv_heads = num_kv_heads
if self.total_num_kv_heads >= tp_size:
assert self.total_num_kv_heads % tp_size == 0
else:
assert tp_size % self.total_num_kv_heads == 0
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
self.head_dim = hidden_size // self.total_num_heads
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
self.scaling = self.head_dim ** -0.5
self.rope_theta = rope_theta
self.max_position_embeddings = max_position_embeddings
self.qkv_proj = QKVParallelLinear(hidden_size, self.head_dim, self.
total_num_heads, self.total_num_kv_heads, bias=False, linear_method=
linear_method)
self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
hidden_size, bias=False, linear_method=linear_method)
self.rotary_emb = get_rope(self.head_dim, rotary_dim=self.head_dim,
max_position=max_position_embeddings, base=rope_theta, rope_scaling=
rope_scaling)
self.attn = PagedAttention(self.num_heads, self.head_dim, self.scaling,
num_kv_heads=self.num_kv_heads) | def __init__(self, hidden_size: int, num_heads: int, num_kv_heads: int,
rope_theta: float=10000, rope_scaling: Optional[Dict[str, Any]]=None,
max_position_embeddings: int=8192, linear_method: Optional[
LinearMethodBase]=None) ->None:
super().__init__()
self.hidden_size = hidden_size
tp_size = get_tensor_model_parallel_world_size()
self.total_num_heads = num_heads
assert self.total_num_heads % tp_size == 0
self.num_heads = self.total_num_heads // tp_size
self.total_num_kv_heads = num_kv_heads
if self.total_num_kv_heads >= tp_size:
assert self.total_num_kv_heads % tp_size == 0
else:
assert tp_size % self.total_num_kv_heads == 0
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
self.head_dim = hidden_size // self.total_num_heads
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
self.scaling = self.head_dim ** -0.5
self.rope_theta = rope_theta
self.max_position_embeddings = max_position_embeddings
self.qkv_proj = QKVParallelLinear(hidden_size, self.head_dim, self.
total_num_heads, self.total_num_kv_heads, bias=False, linear_method
=linear_method)
self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
hidden_size, bias=False, linear_method=linear_method)
self.rotary_emb = get_rope(self.head_dim, rotary_dim=self.head_dim,
max_position=max_position_embeddings, base=rope_theta, rope_scaling
=rope_scaling)
self.attn = PagedAttention(self.num_heads, self.head_dim, self.scaling,
num_kv_heads=self.num_kv_heads) | null |
forward | return self.act(x) / self.scales | def forward(self, x: torch.Tensor) ->torch.Tensor:
return self.act(x) / self.scales | null |
get_model_config | """Gets the model configuration."""
return self.model_config | def get_model_config(self) ->ModelConfig:
"""Gets the model configuration."""
return self.model_config | Gets the model configuration. |
__init__ | super().__init__()
self.config = config
self.linear_method = linear_method
self.transformer = PhiModel(config, linear_method)
self.lm_head = PhiCausalLMHead(config)
self.sampler = Sampler(config.vocab_size) | def __init__(self, config: PretrainedConfig, linear_method: Optional[
LinearMethodBase]=None):
super().__init__()
self.config = config
self.linear_method = linear_method
self.transformer = PhiModel(config, linear_method)
self.lm_head = PhiCausalLMHead(config)
self.sampler = Sampler(config.vocab_size) | null |
__init__ | super().__init__()
self.hidden_size = hidden_size
tp_size = get_tensor_model_parallel_world_size()
self.total_num_heads = num_heads
assert self.total_num_heads % tp_size == 0
self.num_heads = self.total_num_heads // tp_size
self.total_num_kv_heads = num_kv_heads
if self.total_num_kv_heads >= tp_size:
assert self.total_num_kv_heads % tp_size == 0
else:
assert tp_size % self.total_num_kv_heads == 0
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
self.head_dim = hidden_size // self.total_num_heads
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
self.scaling = self.head_dim ** -0.5
self.rope_theta = rope_theta
self.max_position_embeddings = max_position_embeddings
self.qkv_proj = QKVParallelLinear(hidden_size, self.head_dim, self.
total_num_heads, self.total_num_kv_heads, bias=False, linear_method=
linear_method)
self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
hidden_size, bias=False, linear_method=linear_method)
self.rotary_emb = get_rope(self.head_dim, rotary_dim=self.head_dim,
max_position=max_position_embeddings, base=self.rope_theta,
rope_scaling=rope_scaling)
self.attn = PagedAttention(self.num_heads, self.head_dim, self.scaling,
num_kv_heads=self.num_kv_heads) | def __init__(self, hidden_size: int, num_heads: int, num_kv_heads: int,
rope_theta: float=10000, rope_scaling: Optional[Dict[str, Any]]=None,
max_position_embeddings: int=8192, linear_method: Optional[
LinearMethodBase]=None) ->None:
super().__init__()
self.hidden_size = hidden_size
tp_size = get_tensor_model_parallel_world_size()
self.total_num_heads = num_heads
assert self.total_num_heads % tp_size == 0
self.num_heads = self.total_num_heads // tp_size
self.total_num_kv_heads = num_kv_heads
if self.total_num_kv_heads >= tp_size:
assert self.total_num_kv_heads % tp_size == 0
else:
assert tp_size % self.total_num_kv_heads == 0
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
self.head_dim = hidden_size // self.total_num_heads
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
self.scaling = self.head_dim ** -0.5
self.rope_theta = rope_theta
self.max_position_embeddings = max_position_embeddings
self.qkv_proj = QKVParallelLinear(hidden_size, self.head_dim, self.
total_num_heads, self.total_num_kv_heads, bias=False, linear_method
=linear_method)
self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
hidden_size, bias=False, linear_method=linear_method)
self.rotary_emb = get_rope(self.head_dim, rotary_dim=self.head_dim,
max_position=max_position_embeddings, base=self.rope_theta,
rope_scaling=rope_scaling)
self.attn = PagedAttention(self.num_heads, self.head_dim, self.scaling,
num_kv_heads=self.num_kv_heads) | null |
get_tokenizer | return self.llm_engine.tokenizer | def get_tokenizer(self) ->Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
return self.llm_engine.tokenizer | null |
__repr__ | return f'PhysicalTokenBlock(device={self.device}, block_number={self.block_number}, ref_count={self.ref_count})' | def __repr__(self) ->str:
return (
f'PhysicalTokenBlock(device={self.device}, block_number={self.block_number}, ref_count={self.ref_count})'
) | null |
generate | """Generates the completions for the input prompts.
NOTE: This class automatically batches the given prompts, considering
the memory constraint. For the best performance, put all of your prompts
into a single list and pass it to this method.
Args:
prompts: A list of prompts to generate completions for.
sampling_params: The sampling parameters for text generation. If
None, we use the default sampling parameters.
prompt_token_ids: A list of token IDs for the prompts. If None, we
use the tokenizer to convert the prompts to token IDs.
use_tqdm: Whether to use tqdm to display the progress bar.
Returns:
A list of `RequestOutput` objects containing the generated
completions in the same order as the input prompts.
"""
if prompts is None and prompt_token_ids is None:
raise ValueError('Either prompts or prompt_token_ids must be provided.')
if isinstance(prompts, str):
prompts = [prompts]
if prompts is not None and prompt_token_ids is not None and len(prompts
) != len(prompt_token_ids):
raise ValueError(
'The lengths of prompts and prompt_token_ids must be the same.')
if sampling_params is None:
sampling_params = SamplingParams()
num_requests = len(prompts) if prompts is not None else len(prompt_token_ids)
for i in range(num_requests):
prompt = prompts[i] if prompts is not None else None
token_ids = None if prompt_token_ids is None else prompt_token_ids[i]
self._add_request(prompt, sampling_params, token_ids)
return self._run_engine(use_tqdm) | def generate(self, prompts: Optional[Union[str, List[str]]]=None,
sampling_params: Optional[SamplingParams]=None, prompt_token_ids:
Optional[List[List[int]]]=None, use_tqdm: bool=True) ->List[RequestOutput]:
"""Generates the completions for the input prompts.
NOTE: This class automatically batches the given prompts, considering
the memory constraint. For the best performance, put all of your prompts
into a single list and pass it to this method.
Args:
prompts: A list of prompts to generate completions for.
sampling_params: The sampling parameters for text generation. If
None, we use the default sampling parameters.
prompt_token_ids: A list of token IDs for the prompts. If None, we
use the tokenizer to convert the prompts to token IDs.
use_tqdm: Whether to use tqdm to display the progress bar.
Returns:
A list of `RequestOutput` objects containing the generated
completions in the same order as the input prompts.
"""
if prompts is None and prompt_token_ids is None:
raise ValueError('Either prompts or prompt_token_ids must be provided.'
)
if isinstance(prompts, str):
prompts = [prompts]
if prompts is not None and prompt_token_ids is not None and len(prompts
) != len(prompt_token_ids):
raise ValueError(
'The lengths of prompts and prompt_token_ids must be the same.')
if sampling_params is None:
sampling_params = SamplingParams()
num_requests = len(prompts) if prompts is not None else len(
prompt_token_ids)
for i in range(num_requests):
prompt = prompts[i] if prompts is not None else None
token_ids = None if prompt_token_ids is None else prompt_token_ids[i]
self._add_request(prompt, sampling_params, token_ids)
return self._run_engine(use_tqdm) | Generates the completions for the input prompts.
NOTE: This class automatically batches the given prompts, considering
the memory constraint. For the best performance, put all of your prompts
into a single list and pass it to this method.
Args:
prompts: A list of prompts to generate completions for.
sampling_params: The sampling parameters for text generation. If
None, we use the default sampling parameters.
prompt_token_ids: A list of token IDs for the prompts. If None, we
use the tokenizer to convert the prompts to token IDs.
use_tqdm: Whether to use tqdm to display the progress bar.
Returns:
A list of `RequestOutput` objects containing the generated
completions in the same order as the input prompts. |
apply_weights | weight = weights['weight']
if self.separate_bias_add:
if bias:
return F.linear(x, weight) + bias
return F.linear(x, weight)
return F.linear(x, weight, bias) | def apply_weights(self, weights: Dict[str, torch.Tensor], x: torch.Tensor,
bias: Optional[torch.Tensor]=None) ->torch.Tensor:
weight = weights['weight']
if self.separate_bias_add:
if bias:
return F.linear(x, weight) + bias
return F.linear(x, weight)
return F.linear(x, weight, bias) | null |
_verify_args | if self.max_num_batched_tokens < self.max_model_len:
raise ValueError(
f'max_num_batched_tokens ({self.max_num_batched_tokens}) is smaller than max_model_len ({self.max_model_len}). This effectively limits the maximum sequence length to max_num_batched_tokens and makes vLLM reject longer sequences. Please increase max_num_batched_tokens or decrease max_model_len.'
)
if self.max_num_batched_tokens < self.max_num_seqs:
raise ValueError(
f'max_num_batched_tokens ({self.max_num_batched_tokens}) must be greater than or equal to max_num_seqs ({self.max_num_seqs}).'
) | def _verify_args(self) ->None:
if self.max_num_batched_tokens < self.max_model_len:
raise ValueError(
f'max_num_batched_tokens ({self.max_num_batched_tokens}) is smaller than max_model_len ({self.max_model_len}). This effectively limits the maximum sequence length to max_num_batched_tokens and makes vLLM reject longer sequences. Please increase max_num_batched_tokens or decrease max_model_len.'
)
if self.max_num_batched_tokens < self.max_num_seqs:
raise ValueError(
f'max_num_batched_tokens ({self.max_num_batched_tokens}) must be greater than or equal to max_num_seqs ({self.max_num_seqs}).'
) | null |
allocate_gpu_cache | gpu_cache: List[KVCache] = []
key_block_shape = self.get_key_block_shape()
value_block_shape = self.get_value_block_shape()
for _ in range(self.num_layers):
key_blocks = torch.empty(size=(self.num_gpu_blocks, *key_block_shape),
dtype=self.dtype, device='cuda')
value_blocks = torch.empty(size=(self.num_gpu_blocks, *
value_block_shape), dtype=self.dtype, device='cuda')
gpu_cache.append((key_blocks, value_blocks))
return gpu_cache | def allocate_gpu_cache(self) ->List[KVCache]:
gpu_cache: List[KVCache] = []
key_block_shape = self.get_key_block_shape()
value_block_shape = self.get_value_block_shape()
for _ in range(self.num_layers):
key_blocks = torch.empty(size=(self.num_gpu_blocks, *
key_block_shape), dtype=self.dtype, device='cuda')
value_blocks = torch.empty(size=(self.num_gpu_blocks, *
value_block_shape), dtype=self.dtype, device='cuda')
gpu_cache.append((key_blocks, value_blocks))
return gpu_cache | null |
forward | residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
hidden_states = self.self_attn(positions=positions, hidden_states=
hidden_states, kv_cache=kv_cache, input_metadata=input_metadata)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
return hidden_states | def forward(self, positions: torch.Tensor, hidden_states: torch.Tensor,
kv_cache: KVCache, input_metadata: InputMetadata) ->torch.Tensor:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
hidden_states = self.self_attn(positions=positions, hidden_states=
hidden_states, kv_cache=kv_cache, input_metadata=input_metadata)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
return hidden_states | null |
sample | next_tokens = self.sampler(self.lm_head.weight, hidden_states,
sampling_metadata)
return next_tokens | def sample(self, hidden_states: Optional[torch.Tensor], sampling_metadata:
SamplingMetadata) ->Optional[SamplerOutput]:
next_tokens = self.sampler(self.lm_head.weight, hidden_states,
sampling_metadata)
return next_tokens | null |
verify_with_parallel_config | total_num_attention_heads = self.hf_config.num_attention_heads
tensor_parallel_size = parallel_config.tensor_parallel_size
if total_num_attention_heads % tensor_parallel_size != 0:
raise ValueError(
f'Total number of attention heads ({total_num_attention_heads}) must be divisible by tensor parallel size ({tensor_parallel_size}).'
)
total_num_hidden_layers = self.hf_config.num_hidden_layers
pipeline_parallel_size = parallel_config.pipeline_parallel_size
if total_num_hidden_layers % pipeline_parallel_size != 0:
raise ValueError(
f'Total number of hidden layers ({total_num_hidden_layers}) must be divisible by pipeline parallel size ({pipeline_parallel_size}).'
) | def verify_with_parallel_config(self, parallel_config: 'ParallelConfig'
) ->None:
total_num_attention_heads = self.hf_config.num_attention_heads
tensor_parallel_size = parallel_config.tensor_parallel_size
if total_num_attention_heads % tensor_parallel_size != 0:
raise ValueError(
f'Total number of attention heads ({total_num_attention_heads}) must be divisible by tensor parallel size ({tensor_parallel_size}).'
)
total_num_hidden_layers = self.hf_config.num_hidden_layers
pipeline_parallel_size = parallel_config.pipeline_parallel_size
if total_num_hidden_layers % pipeline_parallel_size != 0:
raise ValueError(
f'Total number of hidden layers ({total_num_hidden_layers}) must be divisible by pipeline parallel size ({pipeline_parallel_size}).'
) | null |
get_prompt_len | return len(self.prompt_token_ids) | def get_prompt_len(self) ->int:
return len(self.prompt_token_ids) | null |
get_priority | raise NotImplementedError | def get_priority(self, now: float, seq_group: SequenceGroup) ->float:
raise NotImplementedError | null |
get_value_block_shape | return self.num_heads, self.head_size, self.block_size | def get_value_block_shape(self) ->Tuple[int, int, int]:
return self.num_heads, self.head_size, self.block_size | null |
test_request_tracker | tracker = RequestTracker()
tracker.new_requests_event = DummyEvent()
stream_1 = tracker.add_request('1')
assert tracker.new_requests_event.flag
new, finished = tracker.get_new_and_finished_requests()
assert not tracker.new_requests_event.flag
assert len(new) == 1
assert new[0]['request_id'] == '1'
assert not finished
assert not stream_1.finished
stream_2 = tracker.add_request('2')
stream_3 = tracker.add_request('3')
assert tracker.new_requests_event.flag
new, finished = tracker.get_new_and_finished_requests()
assert not tracker.new_requests_event.flag
assert len(new) == 2
assert new[0]['request_id'] == '2'
assert new[1]['request_id'] == '3'
assert not finished
assert not stream_2.finished
assert not stream_3.finished
with pytest.raises(KeyError):
tracker.add_request('1')
assert not tracker.new_requests_event.flag
tracker.abort_request('1')
new, finished = tracker.get_new_and_finished_requests()
assert len(finished) == 1
assert '1' in finished
assert not new
assert stream_1.finished
stream_4 = tracker.add_request('4')
tracker.abort_request('4')
assert tracker.new_requests_event.flag
new, finished = tracker.get_new_and_finished_requests()
assert len(finished) == 1
assert '4' in finished
assert not new
assert stream_4.finished
stream_5 = tracker.add_request('5')
assert tracker.new_requests_event.flag
tracker.process_request_output(RequestOutput('2', 'output', [], [], [],
finished=True))
new, finished = tracker.get_new_and_finished_requests()
assert not tracker.new_requests_event.flag
assert len(finished) == 1
assert '2' in finished
assert len(new) == 1
assert new[0]['request_id'] == '5'
assert stream_2.finished
assert not stream_5.finished | def test_request_tracker():
tracker = RequestTracker()
tracker.new_requests_event = DummyEvent()
stream_1 = tracker.add_request('1')
assert tracker.new_requests_event.flag
new, finished = tracker.get_new_and_finished_requests()
assert not tracker.new_requests_event.flag
assert len(new) == 1
assert new[0]['request_id'] == '1'
assert not finished
assert not stream_1.finished
stream_2 = tracker.add_request('2')
stream_3 = tracker.add_request('3')
assert tracker.new_requests_event.flag
new, finished = tracker.get_new_and_finished_requests()
assert not tracker.new_requests_event.flag
assert len(new) == 2
assert new[0]['request_id'] == '2'
assert new[1]['request_id'] == '3'
assert not finished
assert not stream_2.finished
assert not stream_3.finished
with pytest.raises(KeyError):
tracker.add_request('1')
assert not tracker.new_requests_event.flag
tracker.abort_request('1')
new, finished = tracker.get_new_and_finished_requests()
assert len(finished) == 1
assert '1' in finished
assert not new
assert stream_1.finished
stream_4 = tracker.add_request('4')
tracker.abort_request('4')
assert tracker.new_requests_event.flag
new, finished = tracker.get_new_and_finished_requests()
assert len(finished) == 1
assert '4' in finished
assert not new
assert stream_4.finished
stream_5 = tracker.add_request('5')
assert tracker.new_requests_event.flag
tracker.process_request_output(RequestOutput('2', 'output', [], [], [],
finished=True))
new, finished = tracker.get_new_and_finished_requests()
assert not tracker.new_requests_event.flag
assert len(finished) == 1
assert '2' in finished
assert len(new) == 1
assert new[0]['request_id'] == '5'
assert stream_2.finished
assert not stream_5.finished | null |
get_tensor_model_parallel_src_rank | """Calculate the global rank corresponding to the first local rank
in the tensor model parallel group."""
global_rank = torch.distributed.get_rank()
local_world_size = get_tensor_model_parallel_world_size()
return global_rank // local_world_size * local_world_size | def get_tensor_model_parallel_src_rank():
"""Calculate the global rank corresponding to the first local rank
in the tensor model parallel group."""
global_rank = torch.distributed.get_rank()
local_world_size = get_tensor_model_parallel_world_size()
return global_rank // local_world_size * local_world_size | Calculate the global rank corresponding to the first local rank
in the tensor model parallel group. |
sort_by_priority | return sorted(seq_groups, key=lambda seq_group: self.get_priority(now,
seq_group), reverse=True) | def sort_by_priority(self, now: float, seq_groups: List[SequenceGroup]) ->List[
SequenceGroup]:
return sorted(seq_groups, key=lambda seq_group: self.get_priority(now,
seq_group), reverse=True) | null |
sample | next_tokens = self.sampler(self.embed_out.weight, hidden_states,
sampling_metadata)
return next_tokens | def sample(self, hidden_states: torch.Tensor, sampling_metadata:
SamplingMetadata) ->Optional[SamplerOutput]:
next_tokens = self.sampler(self.embed_out.weight, hidden_states,
sampling_metadata)
return next_tokens | null |
vocab_range_from_global_vocab_size | per_partition_vocab_size = divide(global_vocab_size, world_size)
return vocab_range_from_per_partition_vocab_size(per_partition_vocab_size, rank
) | def vocab_range_from_global_vocab_size(global_vocab_size: int, rank: int,
world_size: int) ->Sequence[int]:
per_partition_vocab_size = divide(global_vocab_size, world_size)
return vocab_range_from_per_partition_vocab_size(per_partition_vocab_size,
rank) | null |
__init__ | super().__init__()
self.config = config
self.linear_method = linear_method
self.model = MixtralModel(config, linear_method)
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
self.sampler = Sampler(config.vocab_size) | def __init__(self, config: MixtralConfig, linear_method: Optional[
LinearMethodBase]=None) ->None:
super().__init__()
self.config = config
self.linear_method = linear_method
self.model = MixtralModel(config, linear_method)
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
self.sampler = Sampler(config.vocab_size) | null |
__init__ | self.block_size = block_size
self.num_total_gpu_blocks = num_gpu_blocks
self.num_total_cpu_blocks = num_cpu_blocks
self.block_sliding_window = None
if sliding_window is not None:
assert sliding_window % block_size == 0, (sliding_window, block_size)
self.block_sliding_window = sliding_window // block_size
self.watermark = watermark
assert watermark >= 0.0
self.watermark_blocks = int(watermark * num_gpu_blocks)
self.gpu_allocator = BlockAllocator(Device.GPU, block_size, num_gpu_blocks)
self.cpu_allocator = BlockAllocator(Device.CPU, block_size, num_cpu_blocks)
self.block_tables: Dict[int, BlockTable] = {} | def __init__(self, block_size: int, num_gpu_blocks: int, num_cpu_blocks:
int, watermark: float=0.01, sliding_window: Optional[int]=None) ->None:
self.block_size = block_size
self.num_total_gpu_blocks = num_gpu_blocks
self.num_total_cpu_blocks = num_cpu_blocks
self.block_sliding_window = None
if sliding_window is not None:
assert sliding_window % block_size == 0, (sliding_window, block_size)
self.block_sliding_window = sliding_window // block_size
self.watermark = watermark
assert watermark >= 0.0
self.watermark_blocks = int(watermark * num_gpu_blocks)
self.gpu_allocator = BlockAllocator(Device.GPU, block_size, num_gpu_blocks)
self.cpu_allocator = BlockAllocator(Device.CPU, block_size, num_cpu_blocks)
self.block_tables: Dict[int, BlockTable] = {} | null |
_get_logprobs | batched_logprobs_query_seq_indices: List[int] = []
batched_logprobs_query_token_indices: List[int] = []
largest_num_logprobs = 0
sample_idx = 0
for i, (seq_group, sample_result) in enumerate(zip(sampling_metadata.
seq_groups, sample_results)):
seq_ids, sampling_params = seq_group
next_token_ids, parent_ids = sample_result
num_parent_seqs = len(seq_ids)
if (i < sampling_metadata.num_prompts and sampling_params.
prompt_logprobs is not None):
largest_num_logprobs = max(largest_num_logprobs, sampling_params.
prompt_logprobs)
prompt_len = sampling_metadata.prompt_lens[i]
prompt_tokens = sampling_metadata.seq_data[seq_ids[0]].prompt_token_ids
batched_logprobs_query_seq_indices.extend(sample_idx + j for j in
range(prompt_len - 1))
batched_logprobs_query_token_indices.extend(token_id for token_id in
prompt_tokens[1:])
sample_idx += prompt_len - 1
batched_logprobs_query_seq_indices.extend([(sample_idx + parent_id) for
parent_id in parent_ids])
batched_logprobs_query_token_indices.extend(next_token_ids)
if sampling_params.logprobs is not None:
largest_num_logprobs = max(largest_num_logprobs, sampling_params.
logprobs)
sample_idx += num_parent_seqs
assert sample_idx == logprobs.size(0)
batched_logprobs_query_result = logprobs[[
batched_logprobs_query_seq_indices, batched_logprobs_query_token_indices]]
if largest_num_logprobs > 0:
top_logprobs, top_token_ids = torch.topk(logprobs, largest_num_logprobs,
dim=-1)
top_logprobs = top_logprobs.cpu()
top_token_ids = top_token_ids.cpu()
else:
top_logprobs, top_token_ids = None, None
batched_logprobs_query_result = batched_logprobs_query_result.cpu()
result_prompt_logprobs: List[Optional[PromptLogprobs]] = []
result_sample_logprobs: List[SampleLogprobs] = []
sample_idx = 0
query_result_idx = 0
for i, (seq_group, sample_result) in enumerate(zip(sampling_metadata.
seq_groups, sample_results)):
seq_ids, sampling_params = seq_group
next_token_ids, parent_ids = sample_result
if (i < sampling_metadata.num_prompts and sampling_params.
prompt_logprobs is not None):
num_logprobs = sampling_params.prompt_logprobs
prompt_len = sampling_metadata.prompt_lens[i]
prompt_tokens = sampling_metadata.seq_data[seq_ids[0]].prompt_token_ids
group_prompt_logprobs: PromptLogprobs = [None]
for token_id in prompt_tokens[1:]:
prompt_logprobs_dict = {token_id: batched_logprobs_query_result
[query_result_idx].item()}
if num_logprobs > 0:
prompt_logprobs_dict.update(zip(top_token_ids[sample_idx, :
num_logprobs].tolist(), top_logprobs[sample_idx, :
num_logprobs].tolist()))
group_prompt_logprobs.append(prompt_logprobs_dict)
sample_idx += 1
query_result_idx += 1
result_prompt_logprobs.append(group_prompt_logprobs)
else:
result_prompt_logprobs.append(None)
num_logprobs = sampling_params.logprobs
if num_logprobs is None:
num_logprobs = 0
group_sample_logprobs: SampleLogprobs = []
for next_token_id, parent_id in zip(next_token_ids, parent_ids):
sample_logprobs_dict = {next_token_id:
batched_logprobs_query_result[query_result_idx].item()}
query_result_idx += 1
if num_logprobs > 0:
sample_logprobs_dict.update(zip(top_token_ids[sample_idx +
parent_id, :num_logprobs].tolist(), top_logprobs[sample_idx +
parent_id, :num_logprobs].tolist()))
group_sample_logprobs.append(sample_logprobs_dict)
result_sample_logprobs.append(group_sample_logprobs)
sample_idx += len(seq_ids)
return result_prompt_logprobs, result_sample_logprobs | def _get_logprobs(logprobs: torch.Tensor, sampling_metadata:
SamplingMetadata, sample_results: List[Tuple[List[int], List[int]]]
) ->Tuple[List[Optional[List[Optional[Dict[int, float]]]]], List[List[
Dict[int, float]]]]:
batched_logprobs_query_seq_indices: List[int] = []
batched_logprobs_query_token_indices: List[int] = []
largest_num_logprobs = 0
sample_idx = 0
for i, (seq_group, sample_result) in enumerate(zip(sampling_metadata.
seq_groups, sample_results)):
seq_ids, sampling_params = seq_group
next_token_ids, parent_ids = sample_result
num_parent_seqs = len(seq_ids)
if (i < sampling_metadata.num_prompts and sampling_params.
prompt_logprobs is not None):
largest_num_logprobs = max(largest_num_logprobs,
sampling_params.prompt_logprobs)
prompt_len = sampling_metadata.prompt_lens[i]
prompt_tokens = sampling_metadata.seq_data[seq_ids[0]
].prompt_token_ids
batched_logprobs_query_seq_indices.extend(sample_idx + j for j in
range(prompt_len - 1))
batched_logprobs_query_token_indices.extend(token_id for
token_id in prompt_tokens[1:])
sample_idx += prompt_len - 1
batched_logprobs_query_seq_indices.extend([(sample_idx + parent_id) for
parent_id in parent_ids])
batched_logprobs_query_token_indices.extend(next_token_ids)
if sampling_params.logprobs is not None:
largest_num_logprobs = max(largest_num_logprobs,
sampling_params.logprobs)
sample_idx += num_parent_seqs
assert sample_idx == logprobs.size(0)
batched_logprobs_query_result = logprobs[[
batched_logprobs_query_seq_indices,
batched_logprobs_query_token_indices]]
if largest_num_logprobs > 0:
top_logprobs, top_token_ids = torch.topk(logprobs,
largest_num_logprobs, dim=-1)
top_logprobs = top_logprobs.cpu()
top_token_ids = top_token_ids.cpu()
else:
top_logprobs, top_token_ids = None, None
batched_logprobs_query_result = batched_logprobs_query_result.cpu()
result_prompt_logprobs: List[Optional[PromptLogprobs]] = []
result_sample_logprobs: List[SampleLogprobs] = []
sample_idx = 0
query_result_idx = 0
for i, (seq_group, sample_result) in enumerate(zip(sampling_metadata.
seq_groups, sample_results)):
seq_ids, sampling_params = seq_group
next_token_ids, parent_ids = sample_result
if (i < sampling_metadata.num_prompts and sampling_params.
prompt_logprobs is not None):
num_logprobs = sampling_params.prompt_logprobs
prompt_len = sampling_metadata.prompt_lens[i]
prompt_tokens = sampling_metadata.seq_data[seq_ids[0]
].prompt_token_ids
group_prompt_logprobs: PromptLogprobs = [None]
for token_id in prompt_tokens[1:]:
prompt_logprobs_dict = {token_id:
batched_logprobs_query_result[query_result_idx].item()}
if num_logprobs > 0:
prompt_logprobs_dict.update(zip(top_token_ids[
sample_idx, :num_logprobs].tolist(), top_logprobs[
sample_idx, :num_logprobs].tolist()))
group_prompt_logprobs.append(prompt_logprobs_dict)
sample_idx += 1
query_result_idx += 1
result_prompt_logprobs.append(group_prompt_logprobs)
else:
result_prompt_logprobs.append(None)
num_logprobs = sampling_params.logprobs
if num_logprobs is None:
num_logprobs = 0
group_sample_logprobs: SampleLogprobs = []
for next_token_id, parent_id in zip(next_token_ids, parent_ids):
sample_logprobs_dict = {next_token_id:
batched_logprobs_query_result[query_result_idx].item()}
query_result_idx += 1
if num_logprobs > 0:
sample_logprobs_dict.update(zip(top_token_ids[sample_idx +
parent_id, :num_logprobs].tolist(), top_logprobs[
sample_idx + parent_id, :num_logprobs].tolist()))
group_sample_logprobs.append(sample_logprobs_dict)
result_sample_logprobs.append(group_sample_logprobs)
sample_idx += len(seq_ids)
return result_prompt_logprobs, result_sample_logprobs | null |
create_token_type_ids_from_sequences | """
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
sequence pair mask has the following format:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
if token_ids_1 is None, only returns the first portion of the mask (0s).
Args:
token_ids_0 (`List[int]`):
List of ids.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
if token_ids_1 is not None:
output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
return output | def create_token_type_ids_from_sequences(self, token_ids_0: List[int],
token_ids_1: Optional[List[int]]=None) ->List[int]:
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
sequence pair mask has the following format:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
if token_ids_1 is None, only returns the first portion of the mask (0s).
Args:
token_ids_0 (`List[int]`):
List of ids.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
if token_ids_1 is not None:
output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
return output | Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
sequence pair mask has the following format:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
if token_ids_1 is None, only returns the first portion of the mask (0s).
Args:
token_ids_0 (`List[int]`):
List of ids.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). |
forward | qkv, _ = self.qkv_proj(hidden_states)
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
q, k = self.rotary_emb(positions, q, k)
k_cache, v_cache = kv_cache
attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
output, _ = self.o_proj(attn_output)
return output | def forward(self, positions: torch.Tensor, hidden_states: torch.Tensor,
kv_cache: KVCache, input_metadata: InputMetadata) ->torch.Tensor:
qkv, _ = self.qkv_proj(hidden_states)
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
q, k = self.rotary_emb(positions, q, k)
k_cache, v_cache = kv_cache
attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
output, _ = self.o_proj(attn_output)
return output | null |
__init__ | config.num_key_value_heads = max(config.num_key_value_heads_per_layer)
delattr(config, 'num_key_value_heads_per_layer')
super().__init__(config=config, linear_method=linear_method) | def __init__(self, config: Optional[PretrainedConfig]=None, linear_method:
Optional[LinearMethodBase]=None) ->None:
config.num_key_value_heads = max(config.num_key_value_heads_per_layer)
delattr(config, 'num_key_value_heads_per_layer')
super().__init__(config=config, linear_method=linear_method) | null |
get_token_ids | return self.prompt_token_ids + self.output_token_ids | def get_token_ids(self) ->List[int]:
return self.prompt_token_ids + self.output_token_ids | null |
forward | inputs_embeds = self.embedding(input_ids)
hidden_states = self.encoder(hidden_states=inputs_embeds, position_ids=
position_ids, kv_caches=kv_caches, input_metadata=input_metadata)
return hidden_states | def forward(self, input_ids: torch.Tensor, position_ids: torch.Tensor,
kv_caches: List[KVCache], input_metadata: InputMetadata) ->torch.Tensor:
inputs_embeds = self.embedding(input_ids)
hidden_states = self.encoder(hidden_states=inputs_embeds, position_ids=
position_ids, kv_caches=kv_caches, input_metadata=input_metadata)
return hidden_states | null |
generate_greedy_logprobs | all_logprobs = []
for prompt in prompts:
input_ids = self.tokenizer(prompt, return_tensors='pt').input_ids
output = self.model.generate(input_ids.cuda(), use_cache=True,
do_sample=False, max_new_tokens=max_tokens, output_hidden_states=
True, return_dict_in_generate=True)
seq_logprobs = []
for hidden_states in output.hidden_states:
last_hidden_states = hidden_states[-1][0]
logits = torch.matmul(last_hidden_states, self.model.
get_output_embeddings().weight.t())
if self.model.get_output_embeddings().bias is not None:
logits += self.model.get_output_embeddings().bias.unsqueeze(0)
logprobs = torch.nn.functional.log_softmax(logits, dim=-1, dtype=
torch.float32)
seq_logprobs.append(logprobs)
all_logprobs.append(seq_logprobs)
return all_logprobs | def generate_greedy_logprobs(self, prompts: List[str], max_tokens: int) ->List[
List[torch.Tensor]]:
all_logprobs = []
for prompt in prompts:
input_ids = self.tokenizer(prompt, return_tensors='pt').input_ids
output = self.model.generate(input_ids.cuda(), use_cache=True,
do_sample=False, max_new_tokens=max_tokens,
output_hidden_states=True, return_dict_in_generate=True)
seq_logprobs = []
for hidden_states in output.hidden_states:
last_hidden_states = hidden_states[-1][0]
logits = torch.matmul(last_hidden_states, self.model.
get_output_embeddings().weight.t())
if self.model.get_output_embeddings().bias is not None:
logits += self.model.get_output_embeddings().bias.unsqueeze(0)
logprobs = torch.nn.functional.log_softmax(logits, dim=-1,
dtype=torch.float32)
seq_logprobs.append(logprobs)
all_logprobs.append(seq_logprobs)
return all_logprobs | null |
forward | hidden_states = self.wte(input_ids)
residual = None
for i in range(len(self.h)):
layer = self.h[i]
hidden_states, residual = layer(positions, hidden_states, kv_caches[i],
input_metadata, residual)
hidden_states, _ = self.ln_f(hidden_states, residual)
return hidden_states | def forward(self, input_ids: torch.Tensor, positions: torch.Tensor,
kv_caches: List[KVCache], input_metadata: InputMetadata) ->torch.Tensor:
hidden_states = self.wte(input_ids)
residual = None
for i in range(len(self.h)):
layer = self.h[i]
hidden_states, residual = layer(positions, hidden_states, kv_caches
[i], input_metadata, residual)
hidden_states, _ = self.ln_f(hidden_states, residual)
return hidden_states | null |
_get_model_architecture | architectures = getattr(config, 'architectures', [])
for arch in architectures:
model_cls = ModelRegistry.load_model_cls(arch)
if model_cls is not None:
return model_cls
raise ValueError(
f'Model architectures {architectures} are not supported for now. Supported architectures: {ModelRegistry.get_supported_archs()}'
) | def _get_model_architecture(config: PretrainedConfig) ->Type[nn.Module]:
architectures = getattr(config, 'architectures', [])
for arch in architectures:
model_cls = ModelRegistry.load_model_cls(arch)
if model_cls is not None:
return model_cls
raise ValueError(
f'Model architectures {architectures} are not supported for now. Supported architectures: {ModelRegistry.get_supported_archs()}'
) | null |
free | if seq.seq_id not in self.block_tables:
return
block_table = self.block_tables[seq.seq_id]
self._free_block_table(block_table)
del self.block_tables[seq.seq_id] | def free(self, seq: Sequence) ->None:
if seq.seq_id not in self.block_tables:
return
block_table = self.block_tables[seq.seq_id]
self._free_block_table(block_table)
del self.block_tables[seq.seq_id] | null |
build_inputs_with_special_tokens | bos_token_id = [self.bos_token_id] if self.add_bos_token else []
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
output = bos_token_id + token_ids_0 + eos_token_id
if token_ids_1 is not None:
output = output + bos_token_id + token_ids_1 + eos_token_id
return output | def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
output = bos_token_id + token_ids_0 + eos_token_id
if token_ids_1 is not None:
output = output + bos_token_id + token_ids_1 + eos_token_id
return output | null |
_run_engine | if use_tqdm:
num_requests = self.llm_engine.get_num_unfinished_requests()
pbar = tqdm(total=num_requests, desc='Processed prompts')
outputs: List[RequestOutput] = []
while self.llm_engine.has_unfinished_requests():
step_outputs = self.llm_engine.step()
for output in step_outputs:
if output.finished:
outputs.append(output)
if use_tqdm:
pbar.update(1)
if use_tqdm:
pbar.close()
outputs = sorted(outputs, key=lambda x: int(x.request_id))
return outputs | def _run_engine(self, use_tqdm: bool) ->List[RequestOutput]:
if use_tqdm:
num_requests = self.llm_engine.get_num_unfinished_requests()
pbar = tqdm(total=num_requests, desc='Processed prompts')
outputs: List[RequestOutput] = []
while self.llm_engine.has_unfinished_requests():
step_outputs = self.llm_engine.step()
for output in step_outputs:
if output.finished:
outputs.append(output)
if use_tqdm:
pbar.update(1)
if use_tqdm:
pbar.close()
outputs = sorted(outputs, key=lambda x: int(x.request_id))
return outputs | null |
__init__ | super().__init__()
hidden_size = config.hidden_size
inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.attn = GPT2Attention(config, linear_method)
self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.mlp = GPT2MLP(inner_dim, config, linear_method) | def __init__(self, config: GPT2Config, linear_method: Optional[
LinearMethodBase]=None):
super().__init__()
hidden_size = config.hidden_size
inner_dim = (config.n_inner if config.n_inner is not None else 4 *
hidden_size)
self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.attn = GPT2Attention(config, linear_method)
self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.mlp = GPT2MLP(inner_dim, config, linear_method) | null |
forward | batch_size, sequence_length, hidden_dim = hidden_states.shape
hidden_states = hidden_states.view(-1, hidden_dim)
router_logits, _ = self.gate(hidden_states)
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
routing_weights, selected_experts = torch.topk(routing_weights, self.top_k,
dim=-1)
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
final_hidden_states = None
for expert_idx in self.expert_indicies:
expert_layer = self.experts[expert_idx]
expert_mask = selected_experts == expert_idx
expert_weights = (routing_weights * expert_mask).sum(dim=-1, keepdim=True)
current_hidden_states = expert_layer(hidden_states).mul_(expert_weights)
if final_hidden_states is None:
final_hidden_states = current_hidden_states
else:
final_hidden_states.add_(current_hidden_states)
return tensor_model_parallel_all_reduce(final_hidden_states).view(batch_size,
sequence_length, hidden_dim) | def forward(self, hidden_states: torch.Tensor) ->torch.Tensor:
batch_size, sequence_length, hidden_dim = hidden_states.shape
hidden_states = hidden_states.view(-1, hidden_dim)
router_logits, _ = self.gate(hidden_states)
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
routing_weights, selected_experts = torch.topk(routing_weights, self.
top_k, dim=-1)
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
final_hidden_states = None
for expert_idx in self.expert_indicies:
expert_layer = self.experts[expert_idx]
expert_mask = selected_experts == expert_idx
expert_weights = (routing_weights * expert_mask).sum(dim=-1,
keepdim=True)
current_hidden_states = expert_layer(hidden_states).mul_(expert_weights
)
if final_hidden_states is None:
final_hidden_states = current_hidden_states
else:
final_hidden_states.add_(current_hidden_states)
return tensor_model_parallel_all_reduce(final_hidden_states).view(
batch_size, sequence_length, hidden_dim) | null |
__init__ | super().__init__()
self.config = config
assert not config.add_cross_attention
assert not config.scale_attn_by_inverse_layer_idx
assert not config.reorder_and_upcast_attn
self.embed_dim = config.hidden_size
self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim)
self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
self.h = nn.ModuleList([GPT2Block(config, linear_method) for _ in range(
config.num_hidden_layers)])
self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) | def __init__(self, config: GPT2Config, linear_method: Optional[
LinearMethodBase]=None):
super().__init__()
self.config = config
assert not config.add_cross_attention
assert not config.scale_attn_by_inverse_layer_idx
assert not config.reorder_and_upcast_attn
self.embed_dim = config.hidden_size
self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim)
self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
self.h = nn.ModuleList([GPT2Block(config, linear_method) for _ in range
(config.num_hidden_layers)])
self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) | null |
forward | if residual is None:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
else:
hidden_states, residual = self.input_layernorm(hidden_states, residual)
hidden_states = self.self_attn(positions=positions, hidden_states=
hidden_states, kv_cache=kv_cache, input_metadata=input_metadata)
hidden_states, residual = self.post_attention_layernorm(hidden_states, residual
)
hidden_states = self.block_sparse_moe(hidden_states)
return hidden_states, residual | def forward(self, positions: torch.Tensor, hidden_states: torch.Tensor,
kv_cache: KVCache, input_metadata: InputMetadata, residual: Optional[
torch.Tensor]) ->torch.Tensor:
if residual is None:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
else:
hidden_states, residual = self.input_layernorm(hidden_states, residual)
hidden_states = self.self_attn(positions=positions, hidden_states=
hidden_states, kv_cache=kv_cache, input_metadata=input_metadata)
hidden_states, residual = self.post_attention_layernorm(hidden_states,
residual)
hidden_states = self.block_sparse_moe(hidden_states)
return hidden_states, residual | null |
_verify_args | if self.pipeline_parallel_size > 1:
raise NotImplementedError('Pipeline parallelism is not supported yet.') | def _verify_args(self) ->None:
if self.pipeline_parallel_size > 1:
raise NotImplementedError('Pipeline parallelism is not supported yet.') | null |
load_chat_template | if args.chat_template is not None:
try:
with open(args.chat_template, 'r') as f:
chat_template = f.read()
except OSError:
chat_template = codecs.decode(args.chat_template, 'unicode_escape')
tokenizer.chat_template = chat_template
logger.info(f'Using supplied chat template:\n{tokenizer.chat_template}')
elif tokenizer.chat_template is not None:
logger.info(f'Using default chat template:\n{tokenizer.chat_template}')
else:
logger.warning('No chat template provided. Chat API will not work.') | def load_chat_template(args, tokenizer):
if args.chat_template is not None:
try:
with open(args.chat_template, 'r') as f:
chat_template = f.read()
except OSError:
chat_template = codecs.decode(args.chat_template, 'unicode_escape')
tokenizer.chat_template = chat_template
logger.info(f'Using supplied chat template:\n{tokenizer.chat_template}'
)
elif tokenizer.chat_template is not None:
logger.info(f'Using default chat template:\n{tokenizer.chat_template}')
else:
logger.warning('No chat template provided. Chat API will not work.') | null |
__init__ | super().__init__()
self.hidden_size = config.hidden_size
tp_size = get_tensor_model_parallel_world_size()
self.total_num_heads = config.num_attention_heads
assert self.total_num_heads % tp_size == 0
self.num_heads = self.total_num_heads // tp_size
self.multi_query_attention = config.multi_query_attention
self.total_num_kv_heads = (config.multi_query_group_num if config.
multi_query_attention else config.num_attention_heads)
if self.total_num_kv_heads >= tp_size:
assert self.total_num_kv_heads % tp_size == 0
else:
assert tp_size % self.total_num_kv_heads == 0
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
self.head_dim = config.hidden_size // self.total_num_heads
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
self.scaling = self.head_dim ** -0.5
self.query_key_value = QKVParallelLinear(self.hidden_size, self.head_dim,
self.total_num_heads, self.total_num_kv_heads, bias=config.
add_bias_linear or config.add_qkv_bias, linear_method=linear_method)
self.dense = RowParallelLinear(self.total_num_heads * self.head_dim, config
.hidden_size, bias=config.add_bias_linear, linear_method=linear_method)
rope_ratio = getattr(config, 'rope_ratio', 1.0)
max_positions = getattr(config, 'seq_length', 8192)
self.rotary_emb = get_rope(self.head_dim, rotary_dim=self.head_dim // 2,
max_position=max_positions, base=10000 * rope_ratio, is_neox_style=False)
self.attn = PagedAttention(self.num_heads, self.head_dim, self.scaling,
num_kv_heads=self.num_kv_heads) | def __init__(self, config, linear_method: Optional[LinearMethodBase]=None):
super().__init__()
self.hidden_size = config.hidden_size
tp_size = get_tensor_model_parallel_world_size()
self.total_num_heads = config.num_attention_heads
assert self.total_num_heads % tp_size == 0
self.num_heads = self.total_num_heads // tp_size
self.multi_query_attention = config.multi_query_attention
self.total_num_kv_heads = (config.multi_query_group_num if config.
multi_query_attention else config.num_attention_heads)
if self.total_num_kv_heads >= tp_size:
assert self.total_num_kv_heads % tp_size == 0
else:
assert tp_size % self.total_num_kv_heads == 0
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
self.head_dim = config.hidden_size // self.total_num_heads
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
self.scaling = self.head_dim ** -0.5
self.query_key_value = QKVParallelLinear(self.hidden_size, self.
head_dim, self.total_num_heads, self.total_num_kv_heads, bias=
config.add_bias_linear or config.add_qkv_bias, linear_method=
linear_method)
self.dense = RowParallelLinear(self.total_num_heads * self.head_dim,
config.hidden_size, bias=config.add_bias_linear, linear_method=
linear_method)
rope_ratio = getattr(config, 'rope_ratio', 1.0)
max_positions = getattr(config, 'seq_length', 8192)
self.rotary_emb = get_rope(self.head_dim, rotary_dim=self.head_dim // 2,
max_position=max_positions, base=10000 * rope_ratio, is_neox_style=
False)
self.attn = PagedAttention(self.num_heads, self.head_dim, self.scaling,
num_kv_heads=self.num_kv_heads) | null |
can_append_slot | num_free_gpu_blocks = self.gpu_allocator.get_num_free_blocks()
num_seqs = seq_group.num_seqs(status=SequenceStatus.RUNNING)
return num_seqs <= num_free_gpu_blocks | def can_append_slot(self, seq_group: SequenceGroup) ->bool:
num_free_gpu_blocks = self.gpu_allocator.get_num_free_blocks()
num_seqs = seq_group.num_seqs(status=SequenceStatus.RUNNING)
return num_seqs <= num_free_gpu_blocks | null |
vocab_size | """Returns vocab size"""
return self.sp_model.get_piece_size() | @property
def vocab_size(self):
"""Returns vocab size"""
return self.sp_model.get_piece_size() | Returns vocab size |
load_weights | stacked_params_mapping = [('qkv_proj', 'q_proj', 'q'), ('qkv_proj',
'k_proj', 'k'), ('qkv_proj', 'v_proj', 'v'), ('gate_up_proj',
'gate_proj', 0), ('gate_up_proj', 'up_proj', 1)]
params_dict = dict(self.named_parameters())
for name, loaded_weight in hf_model_weights_iterator(model_name_or_path,
cache_dir, load_format, revision):
if 'rotary_emb.inv_freq' in name:
continue
if 'k_proj' in name or 'v_proj' in name:
loaded_weight = self._degroup_weight(loaded_weight)
for param_name, weight_name, shard_id in stacked_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
if name.endswith('.bias') and name not in params_dict:
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
if name.endswith('.bias') and name not in params_dict:
continue
param = params_dict[name]
weight_loader = getattr(param, 'weight_loader', default_weight_loader)
weight_loader(param, loaded_weight) | def load_weights(self, model_name_or_path: str, cache_dir: Optional[str]=
None, load_format: str='auto', revision: Optional[str]=None):
stacked_params_mapping = [('qkv_proj', 'q_proj', 'q'), ('qkv_proj',
'k_proj', 'k'), ('qkv_proj', 'v_proj', 'v'), ('gate_up_proj',
'gate_proj', 0), ('gate_up_proj', 'up_proj', 1)]
params_dict = dict(self.named_parameters())
for name, loaded_weight in hf_model_weights_iterator(model_name_or_path,
cache_dir, load_format, revision):
if 'rotary_emb.inv_freq' in name:
continue
if 'k_proj' in name or 'v_proj' in name:
loaded_weight = self._degroup_weight(loaded_weight)
for param_name, weight_name, shard_id in stacked_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
if name.endswith('.bias') and name not in params_dict:
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
if name.endswith('.bias') and name not in params_dict:
continue
param = params_dict[name]
weight_loader = getattr(param, 'weight_loader',
default_weight_loader)
weight_loader(param, loaded_weight) | null |
_verify_load_format | load_format = self.load_format.lower()
supported_load_format = ['auto', 'pt', 'safetensors', 'npcache', 'dummy']
rocm_not_supported_load_format = []
if load_format not in supported_load_format:
raise ValueError(
f"Unknown load format: {self.load_format}. Must be one of 'auto', 'pt', 'safetensors', 'npcache', or 'dummy'."
)
if is_hip() and load_format in rocm_not_supported_load_format:
rocm_supported_load_format = [f for f in supported_load_format if f not in
rocm_not_supported_load_format]
raise ValueError(
f"load format '{load_format}' is not supported in ROCm. Supported load format are {rocm_supported_load_format}"
)
architectures = getattr(self.hf_config, 'architectures', [])
if 'MixtralForCausalLM' in architectures and load_format == 'pt':
raise ValueError(
"Currently, the 'pt' format is not supported for Mixtral. Please use the 'safetensors' format instead. "
)
self.load_format = load_format | def _verify_load_format(self) ->None:
load_format = self.load_format.lower()
supported_load_format = ['auto', 'pt', 'safetensors', 'npcache', 'dummy']
rocm_not_supported_load_format = []
if load_format not in supported_load_format:
raise ValueError(
f"Unknown load format: {self.load_format}. Must be one of 'auto', 'pt', 'safetensors', 'npcache', or 'dummy'."
)
if is_hip() and load_format in rocm_not_supported_load_format:
rocm_supported_load_format = [f for f in supported_load_format if f
not in rocm_not_supported_load_format]
raise ValueError(
f"load format '{load_format}' is not supported in ROCm. Supported load format are {rocm_supported_load_format}"
)
architectures = getattr(self.hf_config, 'architectures', [])
if 'MixtralForCausalLM' in architectures and load_format == 'pt':
raise ValueError(
"Currently, the 'pt' format is not supported for Mixtral. Please use the 'safetensors' format instead. "
)
self.load_format = load_format | null |
__init__ | super().__init__()
self.config = config
self.vocab_size = config.vocab_size
self.wte = VocabParallelEmbedding(config.vocab_size, config.hidden_size)
self.h = nn.ModuleList([QWenBlock(config, linear_method) for _ in range(
config.num_hidden_layers)])
self.ln_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon) | def __init__(self, config: QWenConfig, linear_method: Optional[
LinearMethodBase]=None):
super().__init__()
self.config = config
self.vocab_size = config.vocab_size
self.wte = VocabParallelEmbedding(config.vocab_size, config.hidden_size)
self.h = nn.ModuleList([QWenBlock(config, linear_method) for _ in range
(config.num_hidden_layers)])
self.ln_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon) | null |
test_decode_streaming | tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
all_input_ids = tokenizer(truth, add_special_tokens=False)['input_ids']
if skip_special_tokens:
all_input_ids = ([tokenizer.bos_token_id] if tokenizer.bos_token_id is not
None else []) + all_input_ids + [tokenizer.eos_token_id]
decoded_text = _run_incremental_decode(tokenizer, all_input_ids,
skip_special_tokens=skip_special_tokens)
assert decoded_text == truth | @pytest.mark.parametrize('truth', TRUTH)
@pytest.mark.parametrize('tokenizer_id', TOKENIZERS)
@pytest.mark.parametrize('skip_special_tokens', (True, False))
def test_decode_streaming(tokenizer_id, truth, skip_special_tokens):
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
all_input_ids = tokenizer(truth, add_special_tokens=False)['input_ids']
if skip_special_tokens:
all_input_ids = ([tokenizer.bos_token_id] if tokenizer.bos_token_id
is not None else []) + all_input_ids + [tokenizer.eos_token_id]
decoded_text = _run_incremental_decode(tokenizer, all_input_ids,
skip_special_tokens=skip_special_tokens)
assert decoded_text == truth | null |
load_weights | stacked_params_mapping = [('qkv_proj', 'q_proj', 'q'), ('qkv_proj',
'k_proj', 'k'), ('qkv_proj', 'v_proj', 'v')]
params_dict = dict(self.named_parameters(remove_duplicate=False))
for name, loaded_weight in hf_model_weights_iterator(model_name_or_path,
cache_dir, load_format, revision):
if 'lm_head.weight' in name:
continue
if name.startswith('decoder.'):
name = 'model.' + name
for param_name, weight_name, shard_id in stacked_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
if name.endswith('.bias') and name not in params_dict:
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
if name.endswith('.bias') and name not in params_dict:
continue
param = params_dict[name]
weight_loader = getattr(param, 'weight_loader', default_weight_loader)
weight_loader(param, loaded_weight) | def load_weights(self, model_name_or_path: str, cache_dir: Optional[str]=
None, load_format: str='auto', revision: Optional[str]=None):
stacked_params_mapping = [('qkv_proj', 'q_proj', 'q'), ('qkv_proj',
'k_proj', 'k'), ('qkv_proj', 'v_proj', 'v')]
params_dict = dict(self.named_parameters(remove_duplicate=False))
for name, loaded_weight in hf_model_weights_iterator(model_name_or_path,
cache_dir, load_format, revision):
if 'lm_head.weight' in name:
continue
if name.startswith('decoder.'):
name = 'model.' + name
for param_name, weight_name, shard_id in stacked_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
if name.endswith('.bias') and name not in params_dict:
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
if name.endswith('.bias') and name not in params_dict:
continue
param = params_dict[name]
weight_loader = getattr(param, 'weight_loader',
default_weight_loader)
weight_loader(param, loaded_weight) | null |
__init__ | super().__init__()
self.hidden_size = hidden_size
tp_size = get_tensor_model_parallel_world_size()
self.total_num_heads = num_heads
assert self.total_num_heads % tp_size == 0
self.num_heads = self.total_num_heads // tp_size
self.total_num_kv_heads = num_kv_heads
if self.total_num_kv_heads >= tp_size:
assert self.total_num_kv_heads % tp_size == 0
else:
assert tp_size % self.total_num_kv_heads == 0
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
self.head_dim = hidden_size // self.total_num_heads
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
self.scaling = self.head_dim ** -0.5
self.rope_theta = rope_theta
self.sliding_window = sliding_window
self.qkv_proj = QKVParallelLinear(hidden_size, self.head_dim, self.
total_num_heads, self.total_num_kv_heads, bias=False, linear_method=
linear_method)
self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
hidden_size, bias=False, linear_method=linear_method)
self.rotary_emb = get_rope(self.head_dim, rotary_dim=self.head_dim,
max_position=max_position, base=int(self.rope_theta), is_neox_style=True)
self.attn = PagedAttention(self.num_heads, self.head_dim, self.scaling,
num_kv_heads=self.num_kv_heads, sliding_window=self.sliding_window) | def __init__(self, hidden_size: int, num_heads: int, num_kv_heads: int,
max_position: int=4096 * 32, rope_theta: float=10000, linear_method:
Optional[LinearMethodBase]=None, sliding_window: Optional[int]=None
) ->None:
super().__init__()
self.hidden_size = hidden_size
tp_size = get_tensor_model_parallel_world_size()
self.total_num_heads = num_heads
assert self.total_num_heads % tp_size == 0
self.num_heads = self.total_num_heads // tp_size
self.total_num_kv_heads = num_kv_heads
if self.total_num_kv_heads >= tp_size:
assert self.total_num_kv_heads % tp_size == 0
else:
assert tp_size % self.total_num_kv_heads == 0
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
self.head_dim = hidden_size // self.total_num_heads
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
self.scaling = self.head_dim ** -0.5
self.rope_theta = rope_theta
self.sliding_window = sliding_window
self.qkv_proj = QKVParallelLinear(hidden_size, self.head_dim, self.
total_num_heads, self.total_num_kv_heads, bias=False, linear_method
=linear_method)
self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
hidden_size, bias=False, linear_method=linear_method)
self.rotary_emb = get_rope(self.head_dim, rotary_dim=self.head_dim,
max_position=max_position, base=int(self.rope_theta), is_neox_style
=True)
self.attn = PagedAttention(self.num_heads, self.head_dim, self.scaling,
num_kv_heads=self.num_kv_heads, sliding_window=self.sliding_window) | null |
_convert_tokens_to_string_with_added_encoders | sub_texts = []
current_sub_text = []
all_special_tokens = set(tokenizer.all_special_tokens)
for token in output_tokens:
if skip_special_tokens and token in all_special_tokens:
continue
if token in tokenizer.get_added_vocab():
if current_sub_text:
sub_text = tokenizer.convert_tokens_to_string(current_sub_text)
sub_texts.append(sub_text)
current_sub_text = []
sub_texts.append(token)
else:
current_sub_text.append(token)
if current_sub_text:
sub_text = tokenizer.convert_tokens_to_string(current_sub_text)
sub_texts.append(sub_text)
if spaces_between_special_tokens:
return ' '.join(sub_texts)
else:
return ''.join(sub_texts) | def _convert_tokens_to_string_with_added_encoders(tokenizer: Union[
PreTrainedTokenizer, PreTrainedTokenizerFast], output_tokens: List[str],
skip_special_tokens: bool, spaces_between_special_tokens: bool) ->str:
sub_texts = []
current_sub_text = []
all_special_tokens = set(tokenizer.all_special_tokens)
for token in output_tokens:
if skip_special_tokens and token in all_special_tokens:
continue
if token in tokenizer.get_added_vocab():
if current_sub_text:
sub_text = tokenizer.convert_tokens_to_string(current_sub_text)
sub_texts.append(sub_text)
current_sub_text = []
sub_texts.append(token)
else:
current_sub_text.append(token)
if current_sub_text:
sub_text = tokenizer.convert_tokens_to_string(current_sub_text)
sub_texts.append(sub_text)
if spaces_between_special_tokens:
return ' '.join(sub_texts)
else:
return ''.join(sub_texts) | null |
forward | del input_
raise RuntimeError("LMHead's weights should be used in the sampler.") | def forward(self, input_):
del input_
raise RuntimeError("LMHead's weights should be used in the sampler.") | null |
execute_method | executor = getattr(self, method)
return executor(*args, **kwargs) | def execute_method(self, method, *args, **kwargs):
executor = getattr(self, method)
return executor(*args, **kwargs) | null |
__init__ | super().__init__()
self.post_layer_norm = config.post_layer_norm
self.num_layers = config.num_layers
self.layers = nn.ModuleList([GLMBlock(config, linear_method) for i in range
(self.num_layers)])
if self.post_layer_norm:
layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
self.final_layernorm = layer_norm_func(config.hidden_size, eps=config.
layernorm_epsilon) | def __init__(self, config, linear_method: Optional[LinearMethodBase]=None):
super().__init__()
self.post_layer_norm = config.post_layer_norm
self.num_layers = config.num_layers
self.layers = nn.ModuleList([GLMBlock(config, linear_method) for i in
range(self.num_layers)])
if self.post_layer_norm:
layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
self.final_layernorm = layer_norm_func(config.hidden_size, eps=
config.layernorm_epsilon) | null |
load_weights | stacked_params_mapping = [('qkv_proj', 'q_proj', 'q'), ('qkv_proj',
'k_proj', 'k'), ('qkv_proj', 'v_proj', 'v')]
params_dict = dict(self.named_parameters())
for name, loaded_weight in hf_model_weights_iterator(model_name_or_path,
cache_dir, load_format, revision, fall_back_to_pt=False):
if 'rotary_emb.inv_freq' in name:
continue
for param_name, weight_name, shard_id in stacked_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
if name.endswith('.bias') and name not in params_dict:
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
if name.endswith('.bias') and name not in params_dict:
continue
if 'block_sparse_moe.experts.' in name and name not in params_dict:
continue
param = params_dict[name]
weight_loader = getattr(param, 'weight_loader', default_weight_loader)
weight_loader(param, loaded_weight) | def load_weights(self, model_name_or_path: str, cache_dir: Optional[str]=
None, load_format: str='auto', revision: Optional[str]=None):
stacked_params_mapping = [('qkv_proj', 'q_proj', 'q'), ('qkv_proj',
'k_proj', 'k'), ('qkv_proj', 'v_proj', 'v')]
params_dict = dict(self.named_parameters())
for name, loaded_weight in hf_model_weights_iterator(model_name_or_path,
cache_dir, load_format, revision, fall_back_to_pt=False):
if 'rotary_emb.inv_freq' in name:
continue
for param_name, weight_name, shard_id in stacked_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
if name.endswith('.bias') and name not in params_dict:
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
if name.endswith('.bias') and name not in params_dict:
continue
if 'block_sparse_moe.experts.' in name and name not in params_dict:
continue
param = params_dict[name]
weight_loader = getattr(param, 'weight_loader',
default_weight_loader)
weight_loader(param, loaded_weight) | null |
forward | layernorm_output = self.input_layernorm(hidden_states)
if self.apply_residual_connection_post_layernorm:
residual = layernorm_output
else:
residual = hidden_states
attention_output = self.self_attention(position_ids=position_ids,
hidden_states=layernorm_output, kv_cache=kv_cache, input_metadata=
input_metadata)
attention_output = attention_output + residual
layernorm_output = self.post_attention_layernorm(attention_output)
if self.apply_residual_connection_post_layernorm:
residual = layernorm_output
else:
residual = attention_output
output = self.mlp(layernorm_output) + residual
return output | def forward(self, position_ids: torch.Tensor, hidden_states: torch.Tensor,
kv_cache: KVCache, input_metadata: InputMetadata) ->torch.Tensor:
layernorm_output = self.input_layernorm(hidden_states)
if self.apply_residual_connection_post_layernorm:
residual = layernorm_output
else:
residual = hidden_states
attention_output = self.self_attention(position_ids=position_ids,
hidden_states=layernorm_output, kv_cache=kv_cache, input_metadata=
input_metadata)
attention_output = attention_output + residual
layernorm_output = self.post_attention_layernorm(attention_output)
if self.apply_residual_connection_post_layernorm:
residual = layernorm_output
else:
residual = attention_output
output = self.mlp(layernorm_output) + residual
return output | null |
create_kv_caches | torch.random.manual_seed(seed)
torch.cuda.manual_seed(seed)
scale = head_size ** -0.5
x = 16 // torch.tensor([], dtype=dtype).element_size()
key_cache_shape = num_blocks, num_heads, head_size // x, block_size, x
key_caches = []
for _ in range(num_layers):
key_cache = torch.empty(size=key_cache_shape, dtype=dtype, device=device)
key_cache.uniform_(-scale, scale)
key_caches.append(key_cache)
value_cache_shape = num_blocks, num_heads, head_size, block_size
value_caches = []
for _ in range(num_layers):
value_cache = torch.empty(size=value_cache_shape, dtype=dtype, device=
device)
value_cache.uniform_(-scale, scale)
value_caches.append(value_cache)
return key_caches, value_caches | def create_kv_caches(num_blocks: int, block_size: int, num_layers: int,
num_heads: int, head_size: int, dtype: torch.dtype, seed: int, device: str
) ->Tuple[List[torch.Tensor], List[torch.Tensor]]:
torch.random.manual_seed(seed)
torch.cuda.manual_seed(seed)
scale = head_size ** -0.5
x = 16 // torch.tensor([], dtype=dtype).element_size()
key_cache_shape = num_blocks, num_heads, head_size // x, block_size, x
key_caches = []
for _ in range(num_layers):
key_cache = torch.empty(size=key_cache_shape, dtype=dtype, device=
device)
key_cache.uniform_(-scale, scale)
key_caches.append(key_cache)
value_cache_shape = num_blocks, num_heads, head_size, block_size
value_caches = []
for _ in range(num_layers):
value_cache = torch.empty(size=value_cache_shape, dtype=dtype,
device=device)
value_cache.uniform_(-scale, scale)
value_caches.append(value_cache)
return key_caches, value_caches | null |
get_num_layers | total_num_hidden_layers = self.hf_config.num_hidden_layers
return total_num_hidden_layers // parallel_config.pipeline_parallel_size | def get_num_layers(self, parallel_config: 'ParallelConfig') ->int:
total_num_hidden_layers = self.hf_config.num_hidden_layers
return total_num_hidden_layers // parallel_config.pipeline_parallel_size | null |
__repr__ | return f'GPTQConfig(weight_bits={self.weight_bits}, group_size={self.group_size}, desc_act={self.desc_act})' | def __repr__(self) ->str:
return (
f'GPTQConfig(weight_bits={self.weight_bits}, group_size={self.group_size}, desc_act={self.desc_act})'
) | null |
get_lock | lock_dir = cache_dir if cache_dir is not None else '/tmp'
lock_file_name = model_name_or_path.replace('/', '-') + '.lock'
lock = filelock.FileLock(os.path.join(lock_dir, lock_file_name))
return lock | def get_lock(model_name_or_path: str, cache_dir: Optional[str]=None):
lock_dir = cache_dir if cache_dir is not None else '/tmp'
lock_file_name = model_name_or_path.replace('/', '-') + '.lock'
lock = filelock.FileLock(os.path.join(lock_dir, lock_file_name))
return lock | null |
get_priority | return now - seq_group.arrival_time | def get_priority(self, now: float, seq_group: SequenceGroup) ->float:
return now - seq_group.arrival_time | null |
forward | with patch('vllm.model_executor.layers.sampler._prune_hidden_states', lambda
x, y: x), patch('vllm.model_executor.layers.sampler._get_logits', lambda
*args, **kwargs: self.fake_logits):
return super().forward(*args, **kwargs) | def forward(self, *args, **kwargs):
with patch('vllm.model_executor.layers.sampler._prune_hidden_states',
lambda x, y: x), patch('vllm.model_executor.layers.sampler._get_logits'
, lambda *args, **kwargs: self.fake_logits):
return super().forward(*args, **kwargs) | null |
get_num_unfinished_seq_groups | return len(self.waiting) + len(self.running) + len(self.swapped) | def get_num_unfinished_seq_groups(self) ->int:
return len(self.waiting) + len(self.running) + len(self.swapped) | null |
stats | """Get the statistics of the engine."""
return JSONResponse(engine.testing_stats()) | @app.get('/stats')
def stats() ->Response:
"""Get the statistics of the engine."""
return JSONResponse(engine.testing_stats()) | Get the statistics of the engine. |
add_request | """Add a request to be sent to the engine on the next background
loop iteration."""
if request_id in self._request_streams:
raise KeyError(f'Request {request_id} already exists.')
stream = AsyncStream(request_id)
self._new_requests.put_nowait((stream, {'request_id': request_id, **
engine_add_request_kwargs}))
self.new_requests_event.set()
return stream | def add_request(self, request_id: str, **engine_add_request_kwargs
) ->AsyncStream:
"""Add a request to be sent to the engine on the next background
loop iteration."""
if request_id in self._request_streams:
raise KeyError(f'Request {request_id} already exists.')
stream = AsyncStream(request_id)
self._new_requests.put_nowait((stream, {'request_id': request_id, **
engine_add_request_kwargs}))
self.new_requests_event.set()
return stream | Add a request to be sent to the engine on the next background
loop iteration. |
__repr__ | return f'SamplingMetadata(seq_groups={self.seq_groups}, seq_data={self.seq_data}, prompt_lens={self.prompt_lens}, selected_token_indices={self.selected_token_indices}, categorized_sample_indices={self.categorized_sample_indices}), perform_sampling={self.perform_sampling})' | def __repr__(self) ->str:
return (
f'SamplingMetadata(seq_groups={self.seq_groups}, seq_data={self.seq_data}, prompt_lens={self.prompt_lens}, selected_token_indices={self.selected_token_indices}, categorized_sample_indices={self.categorized_sample_indices}), perform_sampling={self.perform_sampling})'
) | null |
__init__ | self.output_sizes = output_sizes
tp_size = get_tensor_model_parallel_world_size()
assert all(output_size % tp_size == 0 for output_size in output_sizes)
super().__init__(input_size, sum(output_sizes), bias, gather_output,
skip_bias_add, params_dtype, linear_method) | def __init__(self, input_size: int, output_sizes: List[int], bias: bool=
True, gather_output: bool=False, skip_bias_add: bool=False,
params_dtype: Optional[torch.dtype]=None, linear_method: Optional[
LinearMethodBase]=None):
self.output_sizes = output_sizes
tp_size = get_tensor_model_parallel_world_size()
assert all(output_size % tp_size == 0 for output_size in output_sizes)
super().__init__(input_size, sum(output_sizes), bias, gather_output,
skip_bias_add, params_dtype, linear_method) | null |
__init__ | super().__init__()
self.config = config
self.linear_method = linear_method
assert not config.tie_word_embeddings
self.transformer = GPTJModel(config, linear_method)
self.lm_head = ParallelLMHead(config.vocab_size, config.n_embd, bias=True)
self.sampler = Sampler(config.vocab_size) | def __init__(self, config: GPTJConfig, linear_method: Optional[
LinearMethodBase]=None):
super().__init__()
self.config = config
self.linear_method = linear_method
assert not config.tie_word_embeddings
self.transformer = GPTJModel(config, linear_method)
self.lm_head = ParallelLMHead(config.vocab_size, config.n_embd, bias=True)
self.sampler = Sampler(config.vocab_size) | null |
get_head_size | return self.hf_config.hidden_size // self.hf_config.num_attention_heads | def get_head_size(self) ->int:
return self.hf_config.hidden_size // self.hf_config.num_attention_heads | null |
forward | hidden_states = self.model(input_ids, positions, kv_caches, input_metadata)
return hidden_states | def forward(self, input_ids: torch.Tensor, positions: torch.Tensor,
kv_caches: List[KVCache], input_metadata: InputMetadata) ->torch.Tensor:
hidden_states = self.model(input_ids, positions, kv_caches, input_metadata)
return hidden_states | null |
verify_with_parallel_config | total_cpu_memory = get_cpu_memory()
num_gpus_per_node = parallel_config.tensor_parallel_size
cpu_memory_usage = self.swap_space_bytes * num_gpus_per_node
msg = (
f'{cpu_memory_usage / _GB:.2f} GiB out of the {total_cpu_memory / _GB:.2f} GiB total CPU memory is allocated for the swap space.'
)
if cpu_memory_usage > 0.7 * total_cpu_memory:
raise ValueError('Too large swap space. ' + msg)
elif cpu_memory_usage > 0.4 * total_cpu_memory:
logger.warning('Possibly too large swap space. ' + msg) | def verify_with_parallel_config(self, parallel_config: 'ParallelConfig'
) ->None:
total_cpu_memory = get_cpu_memory()
num_gpus_per_node = parallel_config.tensor_parallel_size
cpu_memory_usage = self.swap_space_bytes * num_gpus_per_node
msg = (
f'{cpu_memory_usage / _GB:.2f} GiB out of the {total_cpu_memory / _GB:.2f} GiB total CPU memory is allocated for the swap space.'
)
if cpu_memory_usage > 0.7 * total_cpu_memory:
raise ValueError('Too large swap space. ' + msg)
elif cpu_memory_usage > 0.4 * total_cpu_memory:
logger.warning('Possibly too large swap space. ' + msg) | null |
_get_and_verify_dtype | config_dtype = getattr(config, 'torch_dtype', None)
if config_dtype is None:
config_dtype = torch.float32
if isinstance(dtype, str):
dtype = dtype.lower()
if dtype == 'auto':
if config_dtype == torch.float32:
torch_dtype = torch.float16
else:
torch_dtype = config_dtype
else:
if dtype not in _STR_DTYPE_TO_TORCH_DTYPE:
raise ValueError(f'Unknown dtype: {dtype}')
torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]
elif isinstance(dtype, torch.dtype):
torch_dtype = dtype
else:
raise ValueError(f'Unknown dtype: {dtype}')
if is_hip() and torch_dtype == torch.float32:
rocm_supported_dtypes = [k for k, v in _STR_DTYPE_TO_TORCH_DTYPE.items(
) if k not in _ROCM_NOT_SUPPORTED_DTYPE]
raise ValueError(
f"dtype '{dtype}' is not supported in ROCm. Supported dtypes are {rocm_supported_dtypes}"
)
if torch_dtype != config_dtype:
if torch_dtype == torch.float32:
pass
elif config_dtype == torch.float32:
pass
else:
logger.warning(f'Casting {config_dtype} to {torch_dtype}.')
return torch_dtype | def _get_and_verify_dtype(config: PretrainedConfig, dtype: Union[str, torch
.dtype]) ->torch.dtype:
config_dtype = getattr(config, 'torch_dtype', None)
if config_dtype is None:
config_dtype = torch.float32
if isinstance(dtype, str):
dtype = dtype.lower()
if dtype == 'auto':
if config_dtype == torch.float32:
torch_dtype = torch.float16
else:
torch_dtype = config_dtype
else:
if dtype not in _STR_DTYPE_TO_TORCH_DTYPE:
raise ValueError(f'Unknown dtype: {dtype}')
torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]
elif isinstance(dtype, torch.dtype):
torch_dtype = dtype
else:
raise ValueError(f'Unknown dtype: {dtype}')
if is_hip() and torch_dtype == torch.float32:
rocm_supported_dtypes = [k for k, v in _STR_DTYPE_TO_TORCH_DTYPE.
items() if k not in _ROCM_NOT_SUPPORTED_DTYPE]
raise ValueError(
f"dtype '{dtype}' is not supported in ROCm. Supported dtypes are {rocm_supported_dtypes}"
)
if torch_dtype != config_dtype:
if torch_dtype == torch.float32:
pass
elif config_dtype == torch.float32:
pass
else:
logger.warning(f'Casting {config_dtype} to {torch_dtype}.')
return torch_dtype | null |
test_copy_blocks | random.seed(seed)
torch.random.manual_seed(seed)
torch.cuda.manual_seed(seed)
gpu_id = f'cuda:{device}'
assert 2 * num_mappings <= num_blocks
src_blocks = random.sample(range(num_blocks), num_mappings)
remainig_blocks = list(set(range(num_blocks)) - set(src_blocks))
dst_blocks = random.sample(remainig_blocks, 2 * num_mappings)
block_mapping = {}
for i in range(num_mappings):
src = src_blocks[i]
dst1 = dst_blocks[2 * i]
dst2 = dst_blocks[2 * i + 1]
block_mapping[src] = [dst1, dst2]
key_caches, value_caches = kv_cache_factory(num_blocks, block_size,
num_layers, num_heads, head_size, dtype, seed, gpu_id)
cloned_key_caches = [key_cache.clone() for key_cache in key_caches]
cloned_value_caches = [value_cache.clone() for value_cache in value_caches]
cache_ops.copy_blocks(key_caches, value_caches, block_mapping)
for src, dsts in block_mapping.items():
for dst in dsts:
for cloned_key_cache in cloned_key_caches:
cloned_key_cache[dst].copy_(cloned_key_cache[src])
for cloned_value_cache in cloned_value_caches:
cloned_value_cache[dst].copy_(cloned_value_cache[src])
for key_cache, cloned_key_cache in zip(key_caches, cloned_key_caches):
assert torch.allclose(key_cache, cloned_key_cache)
for value_cache, cloned_value_cache in zip(value_caches, cloned_value_caches):
assert torch.allclose(value_cache, cloned_value_cache) | @pytest.mark.parametrize('num_mappings', NUM_MAPPINGS)
@pytest.mark.parametrize('num_layers', NUM_LAYERS)
@pytest.mark.parametrize('num_heads', NUM_HEADS)
@pytest.mark.parametrize('head_size', HEAD_SIZES)
@pytest.mark.parametrize('block_size', BLOCK_SIZES)
@pytest.mark.parametrize('num_blocks', NUM_BLOCKS)
@pytest.mark.parametrize('dtype', DTYPES)
@pytest.mark.parametrize('seed', SEEDS)
@pytest.mark.parametrize('device', DEVICES)
@torch.inference_mode()
def test_copy_blocks(kv_cache_factory, num_mappings: int, num_layers: int,
num_heads: int, head_size: int, block_size: int, num_blocks: int, dtype:
torch.dtype, seed: int, device: int) ->None:
random.seed(seed)
torch.random.manual_seed(seed)
torch.cuda.manual_seed(seed)
gpu_id = f'cuda:{device}'
assert 2 * num_mappings <= num_blocks
src_blocks = random.sample(range(num_blocks), num_mappings)
remainig_blocks = list(set(range(num_blocks)) - set(src_blocks))
dst_blocks = random.sample(remainig_blocks, 2 * num_mappings)
block_mapping = {}
for i in range(num_mappings):
src = src_blocks[i]
dst1 = dst_blocks[2 * i]
dst2 = dst_blocks[2 * i + 1]
block_mapping[src] = [dst1, dst2]
key_caches, value_caches = kv_cache_factory(num_blocks, block_size,
num_layers, num_heads, head_size, dtype, seed, gpu_id)
cloned_key_caches = [key_cache.clone() for key_cache in key_caches]
cloned_value_caches = [value_cache.clone() for value_cache in value_caches]
cache_ops.copy_blocks(key_caches, value_caches, block_mapping)
for src, dsts in block_mapping.items():
for dst in dsts:
for cloned_key_cache in cloned_key_caches:
cloned_key_cache[dst].copy_(cloned_key_cache[src])
for cloned_value_cache in cloned_value_caches:
cloned_value_cache[dst].copy_(cloned_value_cache[src])
for key_cache, cloned_key_cache in zip(key_caches, cloned_key_caches):
assert torch.allclose(key_cache, cloned_key_cache)
for value_cache, cloned_value_cache in zip(value_caches,
cloned_value_caches):
assert torch.allclose(value_cache, cloned_value_cache) | null |
forward | input_dtype = hidden_states.dtype
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return (self.weight * hidden_states).to(input_dtype) | def forward(self, hidden_states):
input_dtype = hidden_states.dtype
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.
variance_epsilon)
return (self.weight * hidden_states).to(input_dtype) | null |
find | if seq_id not in self.seqs_dict:
raise ValueError(f'Sequence {seq_id} not found.')
return self.seqs_dict[seq_id] | def find(self, seq_id: int) ->Sequence:
if seq_id not in self.seqs_dict:
raise ValueError(f'Sequence {seq_id} not found.')
return self.seqs_dict[seq_id] | null |
test_silu_and_mul | torch.random.manual_seed(seed)
torch.cuda.manual_seed(seed)
gpu_id = f'cuda:{device}'
x = torch.randn(num_tokens, 2 * d, dtype=dtype, device=gpu_id)
layer = SiluAndMul()
out = layer(x)
ref_out = layer._forward(x)
assert torch.allclose(out, ref_out, atol=1e-05, rtol=1e-05) | @pytest.mark.parametrize('num_tokens', NUM_TOKENS)
@pytest.mark.parametrize('d', D)
@pytest.mark.parametrize('dtype', DTYPES)
@pytest.mark.parametrize('seed', SEEDS)
@pytest.mark.parametrize('device', DEVICES)
@torch.inference_mode()
def test_silu_and_mul(num_tokens: int, d: int, dtype: torch.dtype, seed:
int, device: int) ->None:
torch.random.manual_seed(seed)
torch.cuda.manual_seed(seed)
gpu_id = f'cuda:{device}'
x = torch.randn(num_tokens, 2 * d, dtype=dtype, device=gpu_id)
layer = SiluAndMul()
out = layer(x)
ref_out = layer._forward(x)
assert torch.allclose(out, ref_out, atol=1e-05, rtol=1e-05) | null |
__init__ | super().__init__()
self.hidden_size = config.hidden_size
total_num_heads = config.num_attention_heads
self.tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
assert total_num_heads % self.tensor_model_parallel_world_size == 0
self.num_heads = total_num_heads // self.tensor_model_parallel_world_size
self.head_dim = self.hidden_size // total_num_heads
self.scale = self.head_dim ** -0.5
self.multi_query = config.multi_query
if self.multi_query:
total_num_kv_heads = 1
self.num_kv_heads = 1
else:
total_num_kv_heads = total_num_heads
self.num_kv_heads = self.num_heads
self.kv_dim = self.head_dim * self.num_kv_heads
self.c_attn = QKVParallelLinear(self.hidden_size, self.head_dim,
total_num_heads, total_num_kv_heads, bias=True, linear_method=linear_method
)
self.c_proj = RowParallelLinear(self.hidden_size, self.hidden_size, bias=
True, linear_method=linear_method)
self.attn = PagedAttention(self.num_heads, self.head_dim, scale=self.scale,
num_kv_heads=self.num_kv_heads) | def __init__(self, config: GPTBigCodeConfig, linear_method: Optional[
LinearMethodBase]=None):
super().__init__()
self.hidden_size = config.hidden_size
total_num_heads = config.num_attention_heads
self.tensor_model_parallel_world_size = (
get_tensor_model_parallel_world_size())
assert total_num_heads % self.tensor_model_parallel_world_size == 0
self.num_heads = total_num_heads // self.tensor_model_parallel_world_size
self.head_dim = self.hidden_size // total_num_heads
self.scale = self.head_dim ** -0.5
self.multi_query = config.multi_query
if self.multi_query:
total_num_kv_heads = 1
self.num_kv_heads = 1
else:
total_num_kv_heads = total_num_heads
self.num_kv_heads = self.num_heads
self.kv_dim = self.head_dim * self.num_kv_heads
self.c_attn = QKVParallelLinear(self.hidden_size, self.head_dim,
total_num_heads, total_num_kv_heads, bias=True, linear_method=
linear_method)
self.c_proj = RowParallelLinear(self.hidden_size, self.hidden_size,
bias=True, linear_method=linear_method)
self.attn = PagedAttention(self.num_heads, self.head_dim, scale=self.
scale, num_kv_heads=self.num_kv_heads) | null |
forward | residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_output = self.attn(position_ids=position_ids, hidden_states=
hidden_states, kv_cache=kv_cache, input_metadata=input_metadata)
mlp_output = self.mlp(hidden_states)
hidden_states = attn_output + mlp_output + residual
return hidden_states | def forward(self, position_ids: torch.Tensor, hidden_states: torch.Tensor,
kv_cache: KVCache, input_metadata: InputMetadata) ->torch.Tensor:
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_output = self.attn(position_ids=position_ids, hidden_states=
hidden_states, kv_cache=kv_cache, input_metadata=input_metadata)
mlp_output = self.mlp(hidden_states)
hidden_states = attn_output + mlp_output + residual
return hidden_states | null |
_sample | categorized_seq_group_ids = {t: [] for t in SamplingType}
categorized_sample_indices = sampling_metadata.categorized_sample_indices
for i, seq_group in enumerate(sampling_metadata.seq_groups):
_, sampling_params = seq_group
sampling_type = sampling_params.sampling_type
categorized_seq_group_ids[sampling_type].append(i)
sample_results_dict: Dict[int, Tuple[List[int], List[int]]] = {}
sample_metadata = {}
for sampling_type in SamplingType:
sample_indices = categorized_sample_indices[sampling_type]
num_tokens = len(sample_indices)
if num_tokens == 0:
continue
seq_group_ids = categorized_seq_group_ids[sampling_type]
seq_groups = [sampling_metadata.seq_groups[i] for i in seq_group_ids]
is_prompts = [(i < sampling_metadata.num_prompts) for i in seq_group_ids]
sample_metadata[sampling_type
] = seq_group_ids, seq_groups, is_prompts, sample_indices
if sampling_type == SamplingType.GREEDY:
greedy_samples = torch.argmax(logprobs[sample_indices], dim=-1)
elif sampling_type == SamplingType.RANDOM:
max_best_of = 1
for seq_group, is_prompt in zip(seq_groups, is_prompts):
if is_prompt:
_, sampling_params = seq_group
max_best_of = max(max_best_of, sampling_params.best_of)
multinomial_samples = _multinomial(probs[sample_indices], max_best_of)
elif sampling_type == SamplingType.BEAM:
beam_search_logprobs = logprobs[sample_indices]
else:
raise ValueError(f'Unsupported sampling type: {sampling_type}')
for sampling_type in SamplingType:
if sampling_type not in sample_metadata:
continue
seq_group_ids, seq_groups, is_prompts, sample_indices = sample_metadata[
sampling_type]
if sampling_type == SamplingType.GREEDY:
sample_results = _greedy_sample(seq_groups, greedy_samples)
elif sampling_type == SamplingType.RANDOM:
sample_results = _random_sample(seq_groups, is_prompts,
multinomial_samples)
elif sampling_type == SamplingType.BEAM:
sample_results = _beam_search_sample(seq_groups, is_prompts,
sampling_metadata.seq_data, beam_search_logprobs)
sample_results_dict.update(zip(seq_group_ids, sample_results))
sample_results = [sample_results_dict[i] for i in range(len(
sampling_metadata.seq_groups))]
return sample_results | def _sample(probs: torch.Tensor, logprobs: torch.Tensor, sampling_metadata:
SamplingMetadata) ->List[Tuple[List[int], List[int]]]:
categorized_seq_group_ids = {t: [] for t in SamplingType}
categorized_sample_indices = sampling_metadata.categorized_sample_indices
for i, seq_group in enumerate(sampling_metadata.seq_groups):
_, sampling_params = seq_group
sampling_type = sampling_params.sampling_type
categorized_seq_group_ids[sampling_type].append(i)
sample_results_dict: Dict[int, Tuple[List[int], List[int]]] = {}
sample_metadata = {}
for sampling_type in SamplingType:
sample_indices = categorized_sample_indices[sampling_type]
num_tokens = len(sample_indices)
if num_tokens == 0:
continue
seq_group_ids = categorized_seq_group_ids[sampling_type]
seq_groups = [sampling_metadata.seq_groups[i] for i in seq_group_ids]
is_prompts = [(i < sampling_metadata.num_prompts) for i in
seq_group_ids]
sample_metadata[sampling_type
] = seq_group_ids, seq_groups, is_prompts, sample_indices
if sampling_type == SamplingType.GREEDY:
greedy_samples = torch.argmax(logprobs[sample_indices], dim=-1)
elif sampling_type == SamplingType.RANDOM:
max_best_of = 1
for seq_group, is_prompt in zip(seq_groups, is_prompts):
if is_prompt:
_, sampling_params = seq_group
max_best_of = max(max_best_of, sampling_params.best_of)
multinomial_samples = _multinomial(probs[sample_indices],
max_best_of)
elif sampling_type == SamplingType.BEAM:
beam_search_logprobs = logprobs[sample_indices]
else:
raise ValueError(f'Unsupported sampling type: {sampling_type}')
for sampling_type in SamplingType:
if sampling_type not in sample_metadata:
continue
seq_group_ids, seq_groups, is_prompts, sample_indices = (
sample_metadata[sampling_type])
if sampling_type == SamplingType.GREEDY:
sample_results = _greedy_sample(seq_groups, greedy_samples)
elif sampling_type == SamplingType.RANDOM:
sample_results = _random_sample(seq_groups, is_prompts,
multinomial_samples)
elif sampling_type == SamplingType.BEAM:
sample_results = _beam_search_sample(seq_groups, is_prompts,
sampling_metadata.seq_data, beam_search_logprobs)
sample_results_dict.update(zip(seq_group_ids, sample_results))
sample_results = [sample_results_dict[i] for i in range(len(
sampling_metadata.seq_groups))]
return sample_results | null |
tensor_model_parallel_gather | """Gather the input tensor across model parallel group.
NOTE: We assume that the input tensor is on the same device across
all the ranks.
"""
world_size = get_tensor_model_parallel_world_size()
if world_size == 1:
return input_
assert -input_.dim() <= dim < input_.dim(
), f'Invalid dim ({dim}) for input tensor with shape {input_.size()}'
if dim < 0:
dim += input_.dim()
if get_tensor_model_parallel_rank() == dst:
gather_list = [torch.empty_like(input_) for _ in range(world_size)]
else:
gather_list = None
torch.distributed.gather(input_, gather_list, dst=dst, group=
get_tensor_model_parallel_group())
if get_tensor_model_parallel_rank() == dst:
output_tensor = torch.cat(gather_list, dim=dim)
else:
output_tensor = None
return output_tensor | def tensor_model_parallel_gather(input_, dst=0, dim=-1):
"""Gather the input tensor across model parallel group.
NOTE: We assume that the input tensor is on the same device across
all the ranks.
"""
world_size = get_tensor_model_parallel_world_size()
if world_size == 1:
return input_
assert -input_.dim() <= dim < input_.dim(
), f'Invalid dim ({dim}) for input tensor with shape {input_.size()}'
if dim < 0:
dim += input_.dim()
if get_tensor_model_parallel_rank() == dst:
gather_list = [torch.empty_like(input_) for _ in range(world_size)]
else:
gather_list = None
torch.distributed.gather(input_, gather_list, dst=dst, group=
get_tensor_model_parallel_group())
if get_tensor_model_parallel_rank() == dst:
output_tensor = torch.cat(gather_list, dim=dim)
else:
output_tensor = None
return output_tensor | Gather the input tensor across model parallel group.
NOTE: We assume that the input tensor is on the same device across
all the ranks. |
put | if self._finished:
return
self._queue.put_nowait(item) | def put(self, item: RequestOutput) ->None:
if self._finished:
return
self._queue.put_nowait(item) | null |
__init__ | self.seq_id = seq_id
self.prompt = prompt
self.block_size = block_size
self.data = SequenceData(prompt_token_ids)
self.output_logprobs: SampleLogprobs = []
self.output_text = ''
self.logical_token_blocks: List[LogicalTokenBlock] = []
self._append_tokens_to_blocks(prompt_token_ids)
self.status = SequenceStatus.WAITING
self.prefix_offset = 0
self.read_offset = 0
self.tokens: Optional[List[str]] = None | def __init__(self, seq_id: int, prompt: str, prompt_token_ids: List[int],
block_size: int) ->None:
self.seq_id = seq_id
self.prompt = prompt
self.block_size = block_size
self.data = SequenceData(prompt_token_ids)
self.output_logprobs: SampleLogprobs = []
self.output_text = ''
self.logical_token_blocks: List[LogicalTokenBlock] = []
self._append_tokens_to_blocks(prompt_token_ids)
self.status = SequenceStatus.WAITING
self.prefix_offset = 0
self.read_offset = 0
self.tokens: Optional[List[str]] = None | null |
__init__ | self.request_id = request_id
self._queue = asyncio.Queue()
self._finished = False | def __init__(self, request_id: str) ->None:
self.request_id = request_id
self._queue = asyncio.Queue()
self._finished = False | null |
_init_distributed_environment | """Initialize the distributed environment."""
if torch.distributed.is_initialized():
torch_world_size = torch.distributed.get_world_size()
if torch_world_size != parallel_config.world_size:
raise RuntimeError(
f'torch.distributed is already initialized but the torch world size does not match parallel_config.world_size ({torch_world_size} vs. {parallel_config.world_size}).'
)
elif not distributed_init_method:
raise ValueError(
'distributed_init_method must be set if torch.distributed is not already initialized'
)
else:
torch.distributed.init_process_group(backend='nccl', world_size=
parallel_config.world_size, rank=rank, init_method=
distributed_init_method)
torch.distributed.all_reduce(torch.zeros(1).cuda())
initialize_model_parallel(parallel_config.tensor_parallel_size,
parallel_config.pipeline_parallel_size) | def _init_distributed_environment(parallel_config: ParallelConfig, rank:
int, distributed_init_method: Optional[str]=None) ->None:
"""Initialize the distributed environment."""
if torch.distributed.is_initialized():
torch_world_size = torch.distributed.get_world_size()
if torch_world_size != parallel_config.world_size:
raise RuntimeError(
f'torch.distributed is already initialized but the torch world size does not match parallel_config.world_size ({torch_world_size} vs. {parallel_config.world_size}).'
)
elif not distributed_init_method:
raise ValueError(
'distributed_init_method must be set if torch.distributed is not already initialized'
)
else:
torch.distributed.init_process_group(backend='nccl', world_size=
parallel_config.world_size, rank=rank, init_method=
distributed_init_method)
torch.distributed.all_reduce(torch.zeros(1).cuda())
initialize_model_parallel(parallel_config.tensor_parallel_size,
parallel_config.pipeline_parallel_size) | Initialize the distributed environment. |
get_linear_method | return SqueezeLLMLinearMethod(self) | def get_linear_method(self) ->'SqueezeLLMLinearMethod':
return SqueezeLLMLinearMethod(self) | null |
init_event | self.new_requests_event = asyncio.Event() | def init_event(self):
self.new_requests_event = asyncio.Event() | null |
sample | next_tokens = self.sampler(self.lm_head.weight, hidden_states,
sampling_metadata, self.lm_head.bias)
return next_tokens | def sample(self, hidden_states: torch.Tensor, sampling_metadata:
SamplingMetadata) ->Optional[SamplerOutput]:
next_tokens = self.sampler(self.lm_head.weight, hidden_states,
sampling_metadata, self.lm_head.bias)
return next_tokens | null |
add_request | """Add a request to the engine's request pool.
The request is added to the request pool and will be processed by the
scheduler as `engine.step()` is called. The exact scheduling policy is
determined by the scheduler.
Args:
request_id: The unique ID of the request.
prompt: The prompt string. Can be None if prompt_token_ids is
provided.
sampling_params: The sampling parameters for text generation.
prompt_token_ids: The token IDs of the prompt. If None, we
use the tokenizer to convert the prompts to token IDs.
arrival_time: The arrival time of the request. If None, we use
the current monotonic time.
"""
if arrival_time is None:
arrival_time = time.monotonic()
if prompt_token_ids is None:
assert prompt is not None
prompt_token_ids = self.tokenizer.encode(prompt)
block_size = self.cache_config.block_size
seq_id = next(self.seq_counter)
seq = Sequence(seq_id, prompt, prompt_token_ids, block_size)
seq_group = SequenceGroup(request_id, [seq], sampling_params, arrival_time)
self.scheduler.add_seq_group(seq_group) | def add_request(self, request_id: str, prompt: Optional[str],
sampling_params: SamplingParams, prompt_token_ids: Optional[List[int]]=
None, arrival_time: Optional[float]=None) ->None:
"""Add a request to the engine's request pool.
The request is added to the request pool and will be processed by the
scheduler as `engine.step()` is called. The exact scheduling policy is
determined by the scheduler.
Args:
request_id: The unique ID of the request.
prompt: The prompt string. Can be None if prompt_token_ids is
provided.
sampling_params: The sampling parameters for text generation.
prompt_token_ids: The token IDs of the prompt. If None, we
use the tokenizer to convert the prompts to token IDs.
arrival_time: The arrival time of the request. If None, we use
the current monotonic time.
"""
if arrival_time is None:
arrival_time = time.monotonic()
if prompt_token_ids is None:
assert prompt is not None
prompt_token_ids = self.tokenizer.encode(prompt)
block_size = self.cache_config.block_size
seq_id = next(self.seq_counter)
seq = Sequence(seq_id, prompt, prompt_token_ids, block_size)
seq_group = SequenceGroup(request_id, [seq], sampling_params, arrival_time)
self.scheduler.add_seq_group(seq_group) | Add a request to the engine's request pool.
The request is added to the request pool and will be processed by the
scheduler as `engine.step()` is called. The exact scheduling policy is
determined by the scheduler.
Args:
request_id: The unique ID of the request.
prompt: The prompt string. Can be None if prompt_token_ids is
provided.
sampling_params: The sampling parameters for text generation.
prompt_token_ids: The token IDs of the prompt. If None, we
use the tokenizer to convert the prompts to token IDs.
arrival_time: The arrival time of the request. If None, we use
the current monotonic time. |
get_output_token_ids | return self.data.output_token_ids | def get_output_token_ids(self) ->List[int]:
return self.data.output_token_ids | null |
__init__ | super().__init__()
self.head_size = head_size
self.rotary_dim = rotary_dim
self.max_position_embeddings = max_position_embeddings
self.base = base
self.is_neox_style = is_neox_style
cache = self._compute_cos_sin_cache()
cache = cache.to(torch.get_default_dtype())
self.register_buffer('cos_sin_cache', cache, persistent=False) | def __init__(self, head_size: int, rotary_dim: int, max_position_embeddings:
int, base: int, is_neox_style: bool) ->None:
super().__init__()
self.head_size = head_size
self.rotary_dim = rotary_dim
self.max_position_embeddings = max_position_embeddings
self.base = base
self.is_neox_style = is_neox_style
cache = self._compute_cos_sin_cache()
cache = cache.to(torch.get_default_dtype())
self.register_buffer('cos_sin_cache', cache, persistent=False) | null |
__init__ | self.index = index
self.text = text
self.token_ids = token_ids
self.cumulative_logprob = cumulative_logprob
self.logprobs = logprobs
self.finish_reason = finish_reason | def __init__(self, index: int, text: str, token_ids: List[int],
cumulative_logprob: float, logprobs: Optional[SampleLogprobs],
finish_reason: Optional[str]=None) ->None:
self.index = index
self.text = text
self.token_ids = token_ids
self.cumulative_logprob = cumulative_logprob
self.logprobs = logprobs
self.finish_reason = finish_reason | null |
__init__ | super().__init__()
self.gate_up_proj = MergedColumnParallelLinear(hidden_size, [
intermediate_size] * 2, bias=False, linear_method=linear_method)
self.down_proj = RowParallelLinear(intermediate_size, hidden_size, bias=
False, linear_method=linear_method)
if hidden_act != 'silu':
raise ValueError(
f'Unsupported activation: {hidden_act}. Only silu is supported for now.'
)
self.act_fn = SiluAndMul() | def __init__(self, hidden_size: int, intermediate_size: int, hidden_act:
str, linear_method: Optional[LinearMethodBase]=None):
super().__init__()
self.gate_up_proj = MergedColumnParallelLinear(hidden_size, [
intermediate_size] * 2, bias=False, linear_method=linear_method)
self.down_proj = RowParallelLinear(intermediate_size, hidden_size, bias
=False, linear_method=linear_method)
if hidden_act != 'silu':
raise ValueError(
f'Unsupported activation: {hidden_act}. Only silu is supported for now.'
)
self.act_fn = SiluAndMul() | null |
fork | new_seq = copy.deepcopy(self)
new_seq.seq_id = new_seq_id
return new_seq | def fork(self, new_seq_id: int) ->'Sequence':
new_seq = copy.deepcopy(self)
new_seq.seq_id = new_seq_id
return new_seq | null |
from_config | weight_bits = cls.get_from_keys(config, ['bits'])
group_size = cls.get_from_keys(config, ['group_size'])
desc_act = cls.get_from_keys(config, ['desc_act'])
return cls(weight_bits, group_size, desc_act) | @classmethod
def from_config(cls, config: Dict[str, Any]) ->'GPTQConfig':
weight_bits = cls.get_from_keys(config, ['bits'])
group_size = cls.get_from_keys(config, ['group_size'])
desc_act = cls.get_from_keys(config, ['desc_act'])
return cls(weight_bits, group_size, desc_act) | null |
__init__ | self.device = device
self.block_number = block_number
self.block_size = block_size
self.ref_count = 0 | def __init__(self, device: Device, block_number: int, block_size: int) ->None:
self.device = device
self.block_number = block_number
self.block_size = block_size
self.ref_count = 0 | null |
get_amdgpu_offload_arch | command = '/opt/rocm/llvm/bin/amdgpu-offload-arch'
try:
output = subprocess.check_output([command])
return output.decode('utf-8').strip()
except subprocess.CalledProcessError as e:
error_message = f'Error: {e}'
raise RuntimeError(error_message) from e
except FileNotFoundError as e:
error_message = f'The command {command} was not found.'
raise RuntimeError(error_message) from e
return None | def get_amdgpu_offload_arch():
command = '/opt/rocm/llvm/bin/amdgpu-offload-arch'
try:
output = subprocess.check_output([command])
return output.decode('utf-8').strip()
except subprocess.CalledProcessError as e:
error_message = f'Error: {e}'
raise RuntimeError(error_message) from e
except FileNotFoundError as e:
error_message = f'The command {command} was not found.'
raise RuntimeError(error_message) from e
return None | null |
forward | qkv, _ = self.c_attn(hidden_states)
q, k, v = qkv.chunk(chunks=3, dim=-1)
q, k = self.rotary_emb(positions, q, k)
k_cache, v_cache = kv_cache
attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
output, _ = self.c_proj(attn_output)
return output | def forward(self, positions: torch.Tensor, hidden_states: torch.Tensor,
kv_cache: KVCache, input_metadata: InputMetadata) ->torch.Tensor:
qkv, _ = self.c_attn(hidden_states)
q, k, v = qkv.chunk(chunks=3, dim=-1)
q, k = self.rotary_emb(positions, q, k)
k_cache, v_cache = kv_cache
attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
output, _ = self.c_proj(attn_output)
return output | null |
finished | return self.finish_reason is not None | def finished(self) ->bool:
return self.finish_reason is not None | null |
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