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--- vllm/model_executor/models/deepseek.py 2025-04-03 11:17:01.787109116 +0800
+++ ling_vllm_patch_a.py 2025-04-02 20:53:47.649000000 +0800
@@ -21,7 +21,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only Deepseek model."""
-from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
+from typing import Any, Dict, Iterable, List, Optional, Tuple
import torch
from torch import nn
@@ -29,18 +29,19 @@
from vllm.attention import Attention, AttentionMetadata
from vllm.config import CacheConfig
-from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
+from vllm.distributed import (get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
tensor_model_parallel_all_reduce)
from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.fused_moe import fused_moe
+from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
QKVParallelLinear,
ReplicatedLinear,
RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
-from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.base_config import (
+ QuantizationConfig)
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -49,10 +50,6 @@
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
-from .interfaces import SupportsPP
-from .utils import (is_pp_missing_parameter,
- make_empty_intermediate_tensors_factory, make_layers)
-
class DeepseekMLP(nn.Module):
@@ -91,6 +88,7 @@
def __init__(
self,
config: PretrainedConfig,
+ layer_idx: int,
quant_config: Optional[QuantizationConfig] = None,
):
super().__init__()
@@ -104,15 +102,17 @@
f"Tensor parallel size {self.tp_size} is greater than "
f"the number of experts {self.n_routed_experts}.")
- self.experts = nn.ModuleList([
- DeepseekMLP(hidden_size=config.hidden_size,
- intermediate_size=config.moe_intermediate_size,
- hidden_act=config.hidden_act,
- quant_config=quant_config,
- reduce_results=False)
- for idx in range(self.n_routed_experts)
- ])
- self.pack_params()
+ self.experts = FusedMoE(
+ num_experts=config.n_routed_experts,
+ top_k=config.num_experts_per_tok,
+ hidden_size=config.hidden_size,
+ intermediate_size=config.moe_intermediate_size,
+ reduce_results=False,
+ renormalize=config.norm_topk_prob,
+ quant_config=quant_config,
+ use_grouped_topk=False,
+ prefix=f"model.layers.{layer_idx}.mlp.experts"
+ )
self.gate = ReplicatedLinear(config.hidden_size,
self.n_routed_experts,
@@ -130,25 +130,6 @@
reduce_results=False,
)
- def pack_params(self):
- w1 = []
- w2 = []
- for expert in self.experts:
- w1.append(expert.gate_up_proj.weight)
- w2.append(expert.down_proj.weight)
- self.w1 = torch._utils._flatten_dense_tensors(w1)
- w1s = torch._utils._unflatten_dense_tensors(self.w1, w1)
- for data, param in zip(w1s, w1):
- param.data = data
- self.w1 = self.w1.view(len(w1), *w1s[0].shape)
-
- self.w2 = torch._utils._flatten_dense_tensors(w2)
- w2s = torch._utils._unflatten_dense_tensors(self.w2, w2)
- for data, param in zip(w2s, w2):
- param.data = data
-
- self.w2 = self.w2.view(len(w2), *w2s[0].shape)
-
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
num_tokens, hidden_dim = hidden_states.shape
hidden_states = hidden_states.view(-1, hidden_dim)
@@ -156,18 +137,14 @@
shared_output = self.shared_experts(hidden_states)
# router_logits: (num_tokens, n_experts)
router_logits, _ = self.gate(hidden_states)
- final_hidden_states = fused_moe(hidden_states,
- self.w1,
- self.w2,
- router_logits,
- self.top_k,
- renormalize=self.config.norm_topk_prob,
- inplace=True)
+ final_hidden_states = self.experts(hidden_states=hidden_states,
+ router_logits=router_logits)
- if self.config.n_shared_experts is not None:
+ if shared_output is not None:
final_hidden_states = final_hidden_states + shared_output
- final_hidden_states = tensor_model_parallel_all_reduce(
- final_hidden_states)
+ if self.tp_size > 1:
+ final_hidden_states = tensor_model_parallel_all_reduce(
+ final_hidden_states)
return final_hidden_states.view(num_tokens, hidden_dim)
@@ -179,6 +156,7 @@
hidden_size: int,
num_heads: int,
num_kv_heads: int,
+ head_dim: int,
rope_theta: float = 10000,
rope_scaling: Optional[Dict[str, Any]] = None,
max_position_embeddings: int = 8192,
@@ -201,7 +179,8 @@
# the KV heads across multiple tensor parallel GPUs.
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.head_dim = hidden_size // self.total_num_heads
+ self.head_dim = hidden_size // self.total_num_heads if head_dim is None else head_dim
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
@@ -268,10 +247,12 @@
rope_scaling = getattr(config, "rope_scaling", None)
max_position_embeddings = getattr(config, "max_position_embeddings",
8192)
+ head_dim = getattr(config, "head_dim", None)
self.self_attn = DeepseekAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
num_kv_heads=config.num_key_value_heads,
+ head_dim=head_dim,
rope_theta=rope_theta,
rope_scaling=rope_scaling,
max_position_embeddings=max_position_embeddings,
@@ -281,7 +262,7 @@
if (config.n_routed_experts is not None
and layer_idx >= config.first_k_dense_replace
and layer_idx % config.moe_layer_freq == 0):
- self.mlp = DeepseekMoE(config=config, quant_config=quant_config)
+ self.mlp = DeepseekMoE(config=config, quant_config=quant_config, layer_idx=layer_idx)
else:
self.mlp = DeepseekMLP(
hidden_size=config.hidden_size,
@@ -332,7 +313,6 @@
config: PretrainedConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
- prefix: str = "",
) -> None:
super().__init__()
self.padding_idx = config.pad_token_id
@@ -342,17 +322,14 @@
config.vocab_size,
config.hidden_size,
)
- self.start_layer, self.end_layer, self.layers = make_layers(
- config.num_hidden_layers,
- lambda prefix: DeepseekDecoderLayer(config,
- int(prefix.split(".")[-1]),
- cache_config,
- quant_config=quant_config),
- prefix=f"{prefix}.layers")
+ self.layers = nn.ModuleList([
+ DeepseekDecoderLayer(config,
+ layer_idx,
+ cache_config,
+ quant_config=quant_config)
+ for layer_idx in range(config.num_hidden_layers)
+ ])
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
- self.make_empty_intermediate_tensors = (
- make_empty_intermediate_tensors_factory(
- ["hidden_states", "residual"], config.hidden_size))
def forward(
self,
@@ -360,29 +337,19 @@
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
- intermediate_tensors: Optional[IntermediateTensors],
- ) -> Union[torch.Tensor, IntermediateTensors]:
- if get_pp_group().is_first_rank:
- hidden_states = self.embed_tokens(input_ids)
- residual = None
- else:
- hidden_states = intermediate_tensors["hidden_states"]
- residual = intermediate_tensors["residual"]
- for i in range(self.start_layer, self.end_layer):
+ ) -> torch.Tensor:
+ hidden_states = self.embed_tokens(input_ids)
+ residual = None
+ for i in range(len(self.layers)):
layer = self.layers[i]
hidden_states, residual = layer(positions, hidden_states,
- kv_caches[i - self.start_layer],
- attn_metadata, residual)
- if not get_pp_group().is_last_rank:
- return IntermediateTensors({
- "hidden_states": hidden_states,
- "residual": residual
- })
+ kv_caches[i], attn_metadata,
+ residual)
hidden_states, _ = self.norm(hidden_states, residual)
return hidden_states
-class DeepseekForCausalLM(nn.Module, SupportsPP):
+class DeepseekForCausalLM(nn.Module):
def __init__(
self,
@@ -401,8 +368,6 @@
self.lm_head.weight = self.model.embed_tokens.weight
self.logits_processor = LogitsProcessor(config.vocab_size)
self.sampler = Sampler()
- self.make_empty_intermediate_tensors = (
- self.model.make_empty_intermediate_tensors)
def forward(
self,
@@ -411,9 +376,9 @@
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors] = None,
- ) -> Union[torch.Tensor, IntermediateTensors]:
+ ) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, kv_caches,
- attn_metadata, intermediate_tensors)
+ attn_metadata)
return hidden_states
def compute_logits(
@@ -443,6 +408,15 @@
("gate_up_proj", "up_proj", 1),
]
+ # Params for weights, fp8 weight scales, fp8 activation scales
+ # (param_name, weight_name, expert_id, shard_id)
+ expert_params_mapping = FusedMoE.make_expert_params_mapping(
+ ckpt_gate_proj_name="gate_proj",
+ ckpt_down_proj_name="down_proj",
+ ckpt_up_proj_name="up_proj",
+ num_experts=self.config.n_routed_experts,
+ )
+
params_dict = dict(self.named_parameters())
for name, loaded_weight in weights:
if "rotary_emb.inv_freq" in name:
@@ -450,31 +424,41 @@
for (param_name, weight_name, shard_id) in stacked_params_mapping:
if weight_name not in name:
continue
+ if ("mlp.experts." in name) and name not in params_dict:
+ continue
name = name.replace(weight_name, param_name)
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
- # Skip experts that are not assigned to this worker.
- if (("mlp.experts." in name or "mlp.shared_experts." in name)
- and name not in params_dict):
- continue
- if is_pp_missing_parameter(name, self):
- continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
- # Skip loading extra bias for GPTQ models.
- if name.endswith(".bias") and name not in params_dict:
- continue
- # Skip experts that are not assigned to this worker.
- if (("mlp.experts." in name or "mlp.shared_experts." in name)
- and name not in params_dict):
- continue
- if is_pp_missing_parameter(name, self):
- continue
- param = params_dict[name]
- weight_loader = getattr(param, "weight_loader",
- default_weight_loader)
- weight_loader(param, loaded_weight)
+ for mapping in expert_params_mapping:
+ param_name, weight_name, expert_id, shard_id = mapping
+ if weight_name not in name:
+ continue
+ name = name.replace(weight_name, param_name)
+ param = params_dict[name]
+ weight_loader = param.weight_loader
+ weight_loader(
+ param,
+ loaded_weight,
+ name,
+ shard_id=shard_id,
+ expert_id=expert_id,
+ )
+ break
+ else:
+ # Skip loading extra bias for GPTQ models.
+ if name.endswith(".bias") and name not in params_dict:
+ continue
+ # Skip experts that are not assigned to this worker.
+ if ("mlp.experts." in name or "mlp.shared_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)
\ No newline at end of file
--- vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py 2025-04-03 11:17:01.781109069 +0800
+++ ling_vllm_patch_b.py 2025-04-02 20:54:38.521781433 +0800
@@ -245,7 +245,7 @@
config = self.quant_config.target_scheme_map["Linear"].get("weights")
self.num_bits = config.num_bits
self.packed_factor = 32 // config.num_bits
- self.strategy = config.strategy.value
+ self.strategy = config.strategy
self.group_size = config.group_size
assert config.symmetric, (
"Only symmetric quantization is supported for MoE")
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