mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2026-03-19 10:07:08 +08:00
Merge pull request #18 from jiangkuaixue123/afd-step3-merge
[Feature] adapt step3 model with AFD
This commit is contained in:
jiangkuaixue123
GitHub
commit
93c656e09b
@ -52,9 +52,15 @@ class P2PAFDConnector(AFDConnectorBase):
|
||||
self._need_recv_metadata: bool = True
|
||||
self._tensor_metadata_list: dict[int, TensorMetadata] = {}
|
||||
self._current_afd_connector_metadata: AFDConnectorMetadata | None = None
|
||||
self.num_hidden_layers: int = (
|
||||
self.config.model_config.hf_config.num_hidden_layers
|
||||
)
|
||||
if getattr(self.config.model_config.hf_config, "text_config", None) is not None:
|
||||
self.num_hidden_layers: int = (
|
||||
self.config.model_config.hf_config.text_config.num_hidden_layers
|
||||
)
|
||||
else:
|
||||
self.num_hidden_layers: int = (
|
||||
self.config.model_config.hf_config.num_hidden_layers
|
||||
)
|
||||
|
||||
self.recv_attn_output_counter: int = 0
|
||||
self.recv_ffn_output_counter: int = 0
|
||||
self.dp_metadata_list: dict[int, DPMetadata] = {}
|
||||
@ -175,16 +181,28 @@ class P2PAFDConnector(AFDConnectorBase):
|
||||
tensor_metadata, self._current_afd_connector_metadata
|
||||
)
|
||||
if self.config.parallel_config.data_parallel_size > 1:
|
||||
logger.info("jcz recv_metadata num_of_stages:{}".format(self._current_afd_connector_metadata.num_of_stages))
|
||||
logger.info(
|
||||
"jcz recv_metadata num_of_stages:{}".format(
|
||||
self._current_afd_connector_metadata.num_of_stages
|
||||
)
|
||||
)
|
||||
for stage_idx in range(self._current_afd_connector_metadata.num_of_stages):
|
||||
num_tokens_per_ubatch = self._tensor_metadata_list[stage_idx].size[0]
|
||||
self.dp_metadata_list[stage_idx] = DPMetadata.make(
|
||||
self.config.parallel_config,
|
||||
num_tokens_per_ubatch,
|
||||
torch.tensor([num_tokens_per_ubatch] * self.config.parallel_config.data_parallel_size,
|
||||
device="cpu", dtype=torch.int32),
|
||||
torch.tensor(
|
||||
[num_tokens_per_ubatch]
|
||||
* self.config.parallel_config.data_parallel_size,
|
||||
device="cpu",
|
||||
dtype=torch.int32,
|
||||
),
|
||||
)
|
||||
logger.info("jcz recv_metadata self.dp_metadata_list:{}".format(self.dp_metadata_list))
|
||||
logger.info(
|
||||
"jcz recv_metadata self.dp_metadata_list:{}".format(
|
||||
self.dp_metadata_list
|
||||
)
|
||||
)
|
||||
|
||||
def _send_hidden_states(
|
||||
self,
|
||||
|
||||
@ -1670,7 +1670,7 @@ class DeepseekV2ForCausalLM(
|
||||
return hidden_states
|
||||
|
||||
def compute_ffn_output(
|
||||
self, current_layer_idx, hidden_states
|
||||
self, hidden_states, current_layer_idx
|
||||
) -> torch.Tensor | IntermediateTensors:
|
||||
hidden_states = self.model.compute_ffn_output(hidden_states, current_layer_idx)
|
||||
return hidden_states
|
||||
|
||||
@ -11,12 +11,16 @@ from torch import nn
|
||||
|
||||
from vllm.attention.layer import Attention
|
||||
from vllm.compilation.decorators import support_torch_compile
|
||||
from vllm.config import CacheConfig, ModelConfig, VllmConfig
|
||||
from vllm.config import AFDConfig, CacheConfig, ModelConfig, VllmConfig
|
||||
from vllm.distributed import (
|
||||
get_pp_group,
|
||||
get_tensor_model_parallel_world_size,
|
||||
tensor_model_parallel_all_reduce,
|
||||
)
|
||||
from vllm.distributed.afd_transfer.afd_connector.metadata import (
|
||||
AFDConnectorMetadata,
|
||||
)
|
||||
from vllm.forward_context import AFDMetadata, get_forward_context
|
||||
from vllm.logger import init_logger
|
||||
from vllm.model_executor.layers.activation import SiluAndMul
|
||||
from vllm.model_executor.layers.fused_moe import FusedMoE
|
||||
@ -37,6 +41,7 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
|
||||
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
|
||||
from vllm.sequence import IntermediateTensors
|
||||
from vllm.transformers_utils.configs.step3_vl import Step3TextConfig
|
||||
from vllm.v1.worker.ubatching import dbo_current_ubatch_id, dbo_enabled, dbo_yield
|
||||
|
||||
from .interfaces import SupportsPP
|
||||
from .utils import (
|
||||
@ -228,54 +233,59 @@ class Step3TextDecoderLayer(nn.Module):
|
||||
config: Step3TextConfig,
|
||||
cache_config: CacheConfig | None = None,
|
||||
quant_config: QuantizationConfig | None = None,
|
||||
afd_config: AFDConfig | None = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.hidden_size = config.hidden_size
|
||||
self.afd_role = afd_config.afd_role if afd_config is not None else None
|
||||
|
||||
self.self_attn = Step3TextAttention(
|
||||
hidden_size=self.hidden_size,
|
||||
num_heads=config.num_attention_heads,
|
||||
num_kv_heads=1,
|
||||
cache_config=cache_config,
|
||||
quant_config=quant_config,
|
||||
norm_eps=config.rms_norm_eps,
|
||||
max_position_embedding=config.max_position_embedding,
|
||||
head_dim=config.head_dim,
|
||||
share_q_dim=config.share_q_dim,
|
||||
rope_parameters=config.rope_parameters,
|
||||
prefix=f"{prefix}.self_attn",
|
||||
)
|
||||
|
||||
layer_idx = int(prefix.split("layers.")[1].split(".")[0])
|
||||
moe_layers_enum = getattr(config, "moe_layers_enum", None)
|
||||
if moe_layers_enum is not None:
|
||||
moe_layers_idx = [int(i) for i in moe_layers_enum.strip().split(",")]
|
||||
else:
|
||||
# Default to 1dense.
|
||||
moe_layers_idx = [i for i in range(1, config.num_hidden_layers)]
|
||||
|
||||
if layer_idx in moe_layers_idx:
|
||||
self.moe = FusedMoEBlock(
|
||||
config=config, quant_config=quant_config, prefix=f"{prefix}.moe"
|
||||
)
|
||||
self.share_expert = Step3TextMLP(
|
||||
if self.afd_role is None or self.afd_role == "attention":
|
||||
self.self_attn = Step3TextAttention(
|
||||
hidden_size=self.hidden_size,
|
||||
intermediate_size=config.share_expert_dim,
|
||||
hidden_act="silu",
|
||||
num_heads=config.num_attention_heads,
|
||||
num_kv_heads=1,
|
||||
cache_config=cache_config,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.share_expert",
|
||||
norm_eps=config.rms_norm_eps,
|
||||
max_position_embedding=config.max_position_embedding,
|
||||
head_dim=config.head_dim,
|
||||
share_q_dim=config.share_q_dim,
|
||||
rope_parameters=config.rope_parameters,
|
||||
prefix=f"{prefix}.self_attn",
|
||||
)
|
||||
self.use_moe = True
|
||||
else:
|
||||
self.mlp = Step3TextMLP(
|
||||
hidden_size=config.hidden_size,
|
||||
intermediate_size=config.intermediate_size,
|
||||
hidden_act="silu",
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.mlp",
|
||||
)
|
||||
self.use_moe = False
|
||||
|
||||
self.layer_idx = int(prefix.split("layers.")[1].split(".")[0])
|
||||
|
||||
if self.afd_role is None or self.afd_role == "ffn":
|
||||
moe_layers_enum = getattr(config, "moe_layers_enum", None)
|
||||
if moe_layers_enum is not None:
|
||||
moe_layers_idx = [int(i) for i in moe_layers_enum.strip().split(",")]
|
||||
else:
|
||||
# Default to 1dense.
|
||||
moe_layers_idx = [i for i in range(1, config.num_hidden_layers)]
|
||||
|
||||
if self.layer_idx in moe_layers_idx:
|
||||
self.moe = FusedMoEBlock(
|
||||
config=config, quant_config=quant_config, prefix=f"{prefix}.moe"
|
||||
)
|
||||
self.share_expert = Step3TextMLP(
|
||||
hidden_size=self.hidden_size,
|
||||
intermediate_size=config.share_expert_dim,
|
||||
hidden_act="silu",
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.share_expert",
|
||||
)
|
||||
self.use_moe = True
|
||||
else:
|
||||
self.mlp = Step3TextMLP(
|
||||
hidden_size=config.hidden_size,
|
||||
intermediate_size=config.intermediate_size,
|
||||
hidden_act="silu",
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.mlp",
|
||||
)
|
||||
self.use_moe = False
|
||||
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.post_attention_layernorm = RMSNorm(
|
||||
config.hidden_size, eps=config.rms_norm_eps
|
||||
@ -300,6 +310,9 @@ class Step3TextDecoderLayer(nn.Module):
|
||||
|
||||
hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
|
||||
|
||||
if self.afd_role == "attention":
|
||||
return hidden_states, residual
|
||||
|
||||
if self.use_moe:
|
||||
share_output = self.share_expert(hidden_states)
|
||||
moe_output = self.moe(hidden_states)
|
||||
@ -309,6 +322,16 @@ class Step3TextDecoderLayer(nn.Module):
|
||||
|
||||
return hidden_states, residual
|
||||
|
||||
def compute_ffn_output(self, hidden_states):
|
||||
assert self.afd_role == "ffn"
|
||||
if self.use_moe:
|
||||
share_output = self.share_expert(hidden_states)
|
||||
moe_output = self.moe(hidden_states)
|
||||
hidden_states = share_output + moe_output
|
||||
else:
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
@support_torch_compile
|
||||
class Step3TextModel(nn.Module):
|
||||
@ -317,6 +340,7 @@ class Step3TextModel(nn.Module):
|
||||
config = vllm_config.model_config.hf_config
|
||||
cache_config = vllm_config.cache_config
|
||||
quant_config = vllm_config.quant_config
|
||||
afd_config = vllm_config.afd_config
|
||||
self.vocab_size = config.vocab_size
|
||||
self.config = config
|
||||
|
||||
@ -336,6 +360,7 @@ class Step3TextModel(nn.Module):
|
||||
config=config,
|
||||
cache_config=cache_config,
|
||||
quant_config=quant_config,
|
||||
afd_config=afd_config,
|
||||
prefix=prefix,
|
||||
),
|
||||
prefix=f"{prefix}.layers",
|
||||
@ -352,6 +377,81 @@ class Step3TextModel(nn.Module):
|
||||
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.embed_tokens(input_ids)
|
||||
|
||||
def forward_with_afd(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
residual: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
afd_metadata: AFDMetadata,
|
||||
) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
forward_conext = get_forward_context()
|
||||
recv_handle = None
|
||||
|
||||
ubatch_hidden_states = []
|
||||
ubatch_residual = []
|
||||
|
||||
start_idx = 0
|
||||
for pos in afd_metadata.positions_list:
|
||||
num_tokens = pos.shape[1] if pos.ndim == 2 else pos.shape[0]
|
||||
end_idx = start_idx + num_tokens
|
||||
ubatch_hidden_states.append(hidden_states[start_idx:end_idx])
|
||||
ubatch_residual.append(
|
||||
residual[start_idx:end_idx] if residual is not None else None
|
||||
)
|
||||
start_idx = end_idx
|
||||
|
||||
for layer in islice(self.layers, self.start_layer, self.end_layer):
|
||||
for stage_i in range(forward_conext.afd_metadata.num_of_stages):
|
||||
afd_connector = afd_metadata.afd_connector
|
||||
forward_conext.attn_metadata = afd_metadata.attn_metadata_list[stage_i]
|
||||
forward_conext.dp_metadata = afd_metadata.dp_metadata_list[stage_i]
|
||||
|
||||
residual = ubatch_residual[stage_i]
|
||||
|
||||
if layer.layer_idx > 0:
|
||||
hidden_states, recv_metadata = afd_connector.recv_ffn_output()
|
||||
if recv_metadata.recv_handle_list is not None:
|
||||
recv_handle = recv_metadata.recv_handle_list
|
||||
else:
|
||||
hidden_states = ubatch_hidden_states[stage_i]
|
||||
|
||||
if recv_handle is not None:
|
||||
for work in recv_handle:
|
||||
work.wait()
|
||||
|
||||
current_positions = afd_metadata.positions_list[stage_i]
|
||||
hidden_states, residual = layer(
|
||||
current_positions, hidden_states, residual
|
||||
)
|
||||
|
||||
ubatch_hidden_states[stage_i] = hidden_states
|
||||
ubatch_residual[stage_i] = residual
|
||||
metadata = AFDConnectorMetadata.create_attention_metadata(
|
||||
layer_idx=layer.layer_idx,
|
||||
stage_idx=stage_i,
|
||||
seq_len=hidden_states.shape[0],
|
||||
dtype=hidden_states.dtype,
|
||||
device=hidden_states.device,
|
||||
num_of_stages=afd_metadata.num_of_stages,
|
||||
afd_tokens_lens=afd_metadata.afd_tokens_lens,
|
||||
)
|
||||
afd_connector.send_attn_output(hidden_states, metadata)
|
||||
|
||||
# Recv last layer FFN output.
|
||||
for stage_i in range(afd_metadata.num_of_stages):
|
||||
ubatch_hidden_states[stage_i], recv_metadata = (
|
||||
afd_connector.recv_ffn_output()
|
||||
)
|
||||
|
||||
# Re-assemble the batch
|
||||
hidden_states = torch.cat(ubatch_hidden_states, dim=0)
|
||||
if any(r is not None for r in ubatch_residual):
|
||||
residual = torch.cat(ubatch_residual, dim=0)
|
||||
else:
|
||||
residual = None
|
||||
|
||||
return hidden_states, residual
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
@ -370,8 +470,19 @@ class Step3TextModel(nn.Module):
|
||||
hidden_states = intermediate_tensors["hidden_states"]
|
||||
residual = intermediate_tensors["residual"]
|
||||
|
||||
for layer in islice(self.layers, self.start_layer, self.end_layer):
|
||||
hidden_states, residual = layer(positions, hidden_states, residual)
|
||||
forward_ctx = get_forward_context()
|
||||
afd_metadata = forward_ctx.afd_metadata if forward_ctx is not None else None
|
||||
|
||||
if afd_metadata is not None:
|
||||
hidden_states, residual = self.forward_with_afd(
|
||||
hidden_states,
|
||||
residual,
|
||||
positions,
|
||||
afd_metadata,
|
||||
)
|
||||
else:
|
||||
for layer in islice(self.layers, self.start_layer, self.end_layer):
|
||||
hidden_states, residual = layer(positions, hidden_states, residual)
|
||||
|
||||
if not get_pp_group().is_last_rank:
|
||||
return IntermediateTensors(
|
||||
@ -384,6 +495,14 @@ class Step3TextModel(nn.Module):
|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
return hidden_states
|
||||
|
||||
def compute_ffn_output(
|
||||
self,
|
||||
hidden_states,
|
||||
layer_idx,
|
||||
) -> torch.Tensor | IntermediateTensors:
|
||||
hidden_states = self.layers[layer_idx].compute_ffn_output(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class Step3TextForCausalLM(nn.Module, SupportsPP):
|
||||
def __init__(
|
||||
@ -398,6 +517,11 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
|
||||
self.config = config
|
||||
self.vllm_config = vllm_config
|
||||
|
||||
self.afd_config = vllm_config.afd_config
|
||||
self.afd_role = (
|
||||
self.afd_config.afd_role if self.afd_config is not None else None
|
||||
)
|
||||
|
||||
self.model = Step3TextModel(vllm_config=vllm_config, prefix=prefix)
|
||||
|
||||
if get_pp_group().is_last_rank:
|
||||
@ -429,6 +553,14 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
|
||||
)
|
||||
return hidden_states
|
||||
|
||||
def compute_ffn_output(
|
||||
self,
|
||||
hidden_states,
|
||||
current_layer_idx,
|
||||
) -> torch.Tensor | IntermediateTensors:
|
||||
hidden_states = self.model.compute_ffn_output(hidden_states, current_layer_idx)
|
||||
return hidden_states
|
||||
|
||||
def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
logits = self.logits_processor(self.lm_head, hidden_states)
|
||||
return logits
|
||||
@ -477,9 +609,13 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
|
||||
disable_moe_stacked_params = [data[1] for data in expert_params_mapping]
|
||||
|
||||
for name, loaded_weight in weights:
|
||||
if self.afd_role == "attention" and self.is_moe_weight(name):
|
||||
continue
|
||||
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
|
||||
if any(
|
||||
disable_moe_stacked_param in name
|
||||
for disable_moe_stacked_param in disable_moe_stacked_params
|
||||
@ -498,6 +634,10 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
|
||||
param_name, weight_name, shard_id = mapping
|
||||
if weight_name not in name:
|
||||
continue
|
||||
|
||||
if self.afd_role is not None and self.afd_role == "attention":
|
||||
continue
|
||||
|
||||
name = name.replace(weight_name, param_name)
|
||||
# Skip layers on other devices.
|
||||
if is_pp_missing_parameter(name, self):
|
||||
@ -521,6 +661,12 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
|
||||
loaded_params.add(name)
|
||||
break
|
||||
else:
|
||||
if (
|
||||
self.afd_role == "ffn"
|
||||
and not self.is_moe_weight(name)
|
||||
and not self.is_common_weight(name)
|
||||
):
|
||||
continue
|
||||
for (
|
||||
param_name,
|
||||
weight_name,
|
||||
@ -552,3 +698,25 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(name)
|
||||
return loaded_params
|
||||
|
||||
def is_moe_weight(self, name):
|
||||
if (
|
||||
"shared_expert" in name
|
||||
or "experts" in name
|
||||
or "gate" in name
|
||||
or "up" in name
|
||||
or "down" in name
|
||||
):
|
||||
return True
|
||||
return False
|
||||
|
||||
def is_common_weight(self, name):
|
||||
if (
|
||||
"lm_head" in name
|
||||
or "model.norm.weight" in name
|
||||
or "embed" in name
|
||||
or "input_layernorm" in name
|
||||
or "post_attention_layernorm" in name
|
||||
):
|
||||
return True
|
||||
return False
|
||||
|
||||
@ -1126,6 +1126,16 @@ class Step3VLForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP)
|
||||
|
||||
return hidden_states
|
||||
|
||||
def compute_ffn_output(
|
||||
self,
|
||||
hidden_states,
|
||||
current_layer_idx,
|
||||
) -> torch.Tensor | IntermediateTensors:
|
||||
hidden_states = self.language_model.compute_ffn_output(
|
||||
hidden_states, current_layer_idx
|
||||
)
|
||||
return hidden_states
|
||||
|
||||
def compute_logits(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
|
||||
@ -130,15 +130,17 @@ class GPUFFNModelRunner(LoRAModelRunnerMixin):
|
||||
|
||||
try:
|
||||
hidden_states, recv_metadata = self.connector.recv_attn_output()
|
||||
if hasattr(self.connector, 'dp_metadata_list'):
|
||||
dp_metadata = self.connector.dp_metadata_list.get(recv_metadata.stage_idx, None)
|
||||
if hasattr(self.connector, "dp_metadata_list"):
|
||||
dp_metadata = self.connector.dp_metadata_list.get(
|
||||
recv_metadata.stage_idx, None
|
||||
)
|
||||
else:
|
||||
dp_metadata = None
|
||||
current_layer_idx = recv_metadata.layer_idx
|
||||
logger.info(
|
||||
f"layer {current_layer_idx} moe recv hidden states type:{type(hidden_states)}, shape:{hidden_states.shape}"
|
||||
f" dp_metadata: {dp_metadata}"
|
||||
)
|
||||
# logger.info(
|
||||
# f"layer {current_layer_idx} moe recv hidden states type:{type(hidden_states)}, shape:{hidden_states.shape}"
|
||||
# f" dp_metadata: {dp_metadata}"
|
||||
# )
|
||||
num_tokens = hidden_states.shape[0]
|
||||
if recv_metadata is not None and recv_metadata.recv_handle_list is not None:
|
||||
for work in recv_metadata.recv_handle_list:
|
||||
@ -220,7 +222,7 @@ class GPUFFNModelRunner(LoRAModelRunnerMixin):
|
||||
hidden_states, dim=0
|
||||
)
|
||||
ffn_output = self.model.compute_ffn_output(
|
||||
current_layer_idx, gathered_hidden_states
|
||||
gathered_hidden_states, current_layer_idx
|
||||
)
|
||||
# Extract the output corresponding to current rank
|
||||
start_idx = hidden_states.shape[0] * get_tensor_model_parallel_rank()
|
||||
@ -229,7 +231,7 @@ class GPUFFNModelRunner(LoRAModelRunnerMixin):
|
||||
else:
|
||||
# Single TP case
|
||||
rank_ffn_output = self.model.compute_ffn_output(
|
||||
current_layer_idx, hidden_states
|
||||
hidden_states, current_layer_idx
|
||||
)
|
||||
|
||||
return rank_ffn_output
|
||||
@ -349,7 +351,7 @@ class GPUFFNModelRunner(LoRAModelRunnerMixin):
|
||||
hidden_states, dim=0
|
||||
)
|
||||
ffn_output = self.model.compute_ffn_output(
|
||||
current_layer_idx, gathered_hidden_states
|
||||
gathered_hidden_states, current_layer_idx
|
||||
)
|
||||
|
||||
# Extract the output corresponding to current rank
|
||||
@ -359,7 +361,7 @@ class GPUFFNModelRunner(LoRAModelRunnerMixin):
|
||||
else:
|
||||
# Single TP case
|
||||
rank_ffn_output = self.model.compute_ffn_output(
|
||||
current_layer_idx, hidden_states
|
||||
hidden_states, current_layer_idx
|
||||
)
|
||||
|
||||
return rank_ffn_output
|
||||
|
||||
@ -3141,7 +3141,9 @@ class GPUModelRunner(
|
||||
# Mark KV scales as calculated after the first forward pass
|
||||
self.calculate_kv_scales = False
|
||||
|
||||
afd_metadata = self._build_afd_metadata(ubatch_slices_padded, num_tokens_unpadded)
|
||||
afd_metadata = self._build_afd_metadata(
|
||||
ubatch_slices_padded, num_tokens_unpadded
|
||||
)
|
||||
|
||||
self.profiler.step()
|
||||
# Run the model.
|
||||
@ -3160,8 +3162,9 @@ class GPUModelRunner(
|
||||
record_function_or_nullcontext("gpu_model_runner: forward"),
|
||||
self.maybe_get_kv_connector_output(scheduler_output) as kv_connector_output,
|
||||
):
|
||||
logger.info(f"input_ids: {input_ids.shape}")
|
||||
if inputs_embeds:
|
||||
if input_ids is not None:
|
||||
logger.info(f"input_ids: {input_ids.shape}")
|
||||
if inputs_embeds is not None:
|
||||
logger.info(f"inputs_embeds: {inputs_embeds.shape}")
|
||||
model_output = self._model_forward(
|
||||
input_ids=input_ids,
|
||||
@ -4274,7 +4277,9 @@ class GPUModelRunner(
|
||||
if num_tokens_across_dp is not None:
|
||||
num_tokens_across_dp[:] = num_tokens_padded
|
||||
|
||||
afd_metadata = self._build_afd_metadata(ubatch_slices_padded, num_tokens_unpadded)
|
||||
afd_metadata = self._build_afd_metadata(
|
||||
ubatch_slices_padded, num_tokens_unpadded
|
||||
)
|
||||
|
||||
with (
|
||||
self.maybe_randomize_inputs(input_ids, inputs_embeds),
|
||||
|
||||
@ -405,9 +405,12 @@ class UBatchWrapper:
|
||||
afd_metadata.input_ids_list.append(sliced_input_ids)
|
||||
afd_metadata.positions_list.append(sliced_positions)
|
||||
afd_metadata.inputs_embeds_list.append(sliced_inputs_embeds)
|
||||
afd_metadata.intermediate_tensors_list.append(sliced_intermediate_tensors)
|
||||
afd_metadata.intermediate_tensors_list.append(
|
||||
sliced_intermediate_tensors
|
||||
)
|
||||
afd_metadata.attn_metadata_list.append(
|
||||
attn_metadata[i] if attn_metadata is not None else None)
|
||||
attn_metadata[i] if attn_metadata is not None else None
|
||||
)
|
||||
afd_metadata.dp_metadata_list.append(ubatch_dp_metadata)
|
||||
|
||||
return afd_metadata
|
||||
@ -481,7 +484,7 @@ class UBatchWrapper:
|
||||
# num_tokens, we don't have a non-ubatched one. Without this
|
||||
# check, the cudagraph wrapper will try to capture a cudagraph
|
||||
# for this shape during a normal run.
|
||||
|
||||
|
||||
if cudagraph_runtime_mode is CUDAGraphMode.FULL:
|
||||
assert batch_descriptor is not None
|
||||
if batch_descriptor.num_tokens in self.cudagraphs:
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user