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[Core] Pipeline Parallel Support (#4412)

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Signed-off-by: Muralidhar Andoorveedu <muralidhar.andoorveedu@centml.ai>
This commit is contained in:
Murali Andoorveedu
2024-07-02 10:58:08 -07:00
committed by GitHub
parent 15aba081f3
commit c5832d2ae9
82 changed files with 1096 additions and 400 deletions
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88
vllm/model_executor/models/gpt2.py
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@@ -17,7 +17,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only GPT-2 model compatible with HuggingFace weights."""
from typing import Iterable, List, Optional, Tuple
from typing import Iterable, List, Optional, Tuple, Union
import torch
from torch import nn
@@ -25,7 +25,9 @@ from transformers import GPT2Config
from vllm.attention import Attention, AttentionMetadata
from vllm.config import CacheConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.distributed.parallel_state import (
get_pp_group, get_tensor_model_parallel_world_size)
from vllm.distributed.utils import get_pp_indices
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
QKVParallelLinear,
@@ -38,7 +40,7 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import SamplerOutput
from vllm.sequence import IntermediateTensors, SamplerOutput
class GPT2Attention(nn.Module):
@@ -181,10 +183,18 @@ class GPT2Model(nn.Module):
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, cache_config, quant_config)
for _ in range(config.num_hidden_layers)
])
self.start_layer, self.end_layer = get_pp_indices(
config.num_hidden_layers,
get_pp_group().rank_in_group,
get_pp_group().world_size)
self.h = nn.ModuleList(
[nn.Identity() for _ in range(self.start_layer)] + [
GPT2Block(config, cache_config, quant_config)
for _ in range(self.start_layer, self.end_layer)
] + [
nn.Identity()
for _ in range(self.end_layer, config.num_hidden_layers)
])
self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
def forward(
@@ -193,14 +203,24 @@ class GPT2Model(nn.Module):
position_ids: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
) -> torch.Tensor:
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
hidden_states = inputs_embeds + position_embeds
intermediate_tensors: Optional[IntermediateTensors],
) -> Union[torch.Tensor, IntermediateTensors]:
if get_pp_group().is_first_rank:
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
hidden_states = inputs_embeds + position_embeds
else:
assert intermediate_tensors is not None
hidden_states = intermediate_tensors["hidden_states"]
for i in range(len(self.h)):
for i in range(self.start_layer, self.end_layer):
layer = self.h[i]
hidden_states = layer(hidden_states, kv_caches[i], attn_metadata)
hidden_states = layer(hidden_states,
kv_caches[i - self.start_layer],
attn_metadata)
if not get_pp_group().is_last_rank:
return IntermediateTensors({"hidden_states": hidden_states})
hidden_states = self.ln_f(hidden_states)
return hidden_states
@@ -228,9 +248,10 @@ class GPT2LMHeadModel(nn.Module):
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors] = None,
) -> torch.Tensor:
hidden_states = self.transformer(input_ids, positions, kv_caches,
attn_metadata)
attn_metadata, intermediate_tensors)
return hidden_states
def compute_logits(self, hidden_states: torch.Tensor,
@@ -247,6 +268,16 @@ class GPT2LMHeadModel(nn.Module):
next_tokens = self.sampler(logits, sampling_metadata)
return next_tokens
def make_empty_intermediate_tensors(
self, batch_size: int, dtype: torch.dtype,
device: torch.device) -> IntermediateTensors:
return IntermediateTensors({
"hidden_states":
torch.zeros((batch_size, self.config.hidden_size),
dtype=dtype,
device=device),
})
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
params_dict = dict(self.named_parameters(remove_duplicate=False))
for name, loaded_weight in weights:
@@ -260,16 +291,19 @@ class GPT2LMHeadModel(nn.Module):
continue
if not name.startswith("transformer."):
name = "transformer." + name
param = params_dict[name]
# The HF's GPT-2 implementation uses Conv1D instead of Linear.
# Because of this, we need to transpose the weights.
# Note(zhuohan): the logic below might break quantized models.
for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]:
if conv1d_weight_name not in name:
continue
if not name.endswith(".weight"):
continue
loaded_weight = loaded_weight.t()
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
try:
param = params_dict[name]
# The HF's GPT-2 implementation uses Conv1D instead of Linear.
# Because of this, we need to transpose the weights.
# Note(zhuohan): the logic below might break quantized models.
for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]:
if conv1d_weight_name not in name:
continue
if not name.endswith(".weight"):
continue
loaded_weight = loaded_weight.t()
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
except KeyError:
continue