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concurrently load weights of DeepseekV2ForCausalLM #7943

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Merged
zhaochenyang20 merged 5 commits into from
Jul 15, 2025
Merged

concurrently load weights of DeepseekV2ForCausalLM #7943

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183777e
concurrently load weights of DeepseekV2ForCausalLM
tianyuzhou95 Jul 10, 2025
e7d8d31
Merge branch 'main' into albert/ds-load-concurrent
zhaochenyang20 Jul 12, 2025
92a9bbc
Merge branch 'main' into albert/ds-load-concurrent
tianyuzhou95 Jul 13, 2025
f982761
Merge branch 'main' into albert/ds-load-concurrent
zhaochenyang20 Jul 13, 2025
11f1e2d
Merge branch 'main' into albert/ds-load-concurrent
ispobock Jul 15, 2025
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275 changes: 148 additions & 127 deletions python/sglang/srt/models/deepseek_v2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@
# https://github.com/vllm-project/vllm/blob/fb6af8bc086328ca6659e72d11ffd4309ce4de22/vllm/model_executor/models/deepseek_v2.py
"""Inference-only DeepseekV2 model."""

import concurrent.futures
import logging
import os
from enum import IntEnum, auto
Expand Down Expand Up @@ -2436,154 +2437,174 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]], is_nextn=Fal
assert self.num_fused_shared_experts == 1
log_info_on_rank0(logger, "Shared experts fusion optimization enabled.")

params_dict = dict(self.named_parameters())
weight_names = []
for name, loaded_weight in weights:
if self.num_fused_shared_experts > 0 and "mlp.shared_experts" in name:
name = name.replace(
"mlp.shared_experts",
f"mlp.experts.{self.config.n_routed_experts}",
)
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = []
params_dict = dict(self.named_parameters())
weight_names = []
for name, loaded_weight in weights:
if self.num_fused_shared_experts > 0 and "mlp.shared_experts" in name:
name = name.replace(
"mlp.shared_experts",
f"mlp.experts.{self.config.n_routed_experts}",
)

weight_names.append(name)
weight_names.append(name)

if not is_nextn:
if hasattr(self.config, "num_nextn_predict_layers"):
num_nextn_layers = self.config.num_nextn_predict_layers
if num_nextn_layers > 0 and name.startswith("model.layers"):
name_list = name.split(".")
if (
len(name_list) >= 3
and int(name_list[2]) >= self.config.num_hidden_layers
):
continue
else:
if not name.startswith(nextn_layer_prefix):
continue
if not is_nextn:
if hasattr(self.config, "num_nextn_predict_layers"):
num_nextn_layers = self.config.num_nextn_predict_layers
if num_nextn_layers > 0 and name.startswith("model.layers"):
name_list = name.split(".")
if (
len(name_list) >= 3
and int(name_list[2]) >= self.config.num_hidden_layers
):
continue
else:
if not name.startswith(nextn_layer_prefix):
continue

# Use shared head and embed weights from target model
if "shared_head.head" in name or "embed_tokens" in name:
continue
# Use shared head and embed weights from target model
if "shared_head.head" in name or "embed_tokens" in name:
continue

is_decoder = True
# For nextn specific weights
for weight_name in nextn_spec_weight_names:
if weight_name in name:
name = name.replace(nextn_layer_prefix, "model")
is_decoder = False
break
# For decoder layer weights
if is_decoder:
name = name.replace(nextn_layer_prefix, "model.decoder")

if "rotary_emb.inv_freq" in name:
continue
for param_name, weight_name, shard_id in stacked_params_mapping:
# Skip non-stacked layers and experts (experts handled below).
if weight_name not in name:
is_decoder = True
# For nextn specific weights
for weight_name in nextn_spec_weight_names:
if weight_name in name:
name = name.replace(nextn_layer_prefix, "model")
is_decoder = False
break
# For decoder layer weights
if is_decoder:
name = name.replace(nextn_layer_prefix, "model.decoder")

if "rotary_emb.inv_freq" in name:
continue
# We have mlp.experts[0].gate_proj in the checkpoint.
# Since we handle the experts below in expert_params_mapping,
# we need to skip here BEFORE we update the name, otherwise
# name will be updated to mlp.experts[0].gate_up_proj, which
# will then be updated below in expert_params_mapping
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
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
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
break
else:
for mapping in expert_params_mapping:
param_name, weight_name, expert_id, shard_id = mapping
for param_name, weight_name, shard_id in stacked_params_mapping:
# Skip non-stacked layers and experts (experts handled below).
if weight_name not in name:
continue
# We have mlp.experts[0].gate_proj in the checkpoint.
# Since we handle the experts below in expert_params_mapping,
# we need to skip here BEFORE we update the name, otherwise
# name will be updated to mlp.experts[0].gate_up_proj, which
# will then be updated below in expert_params_mapping
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
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
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(
param,
loaded_weight,
name,
shard_id=shard_id,
expert_id=expert_id,
futures.append(
executor.submit(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
if fuse_qkv_a_proj and (
"q_a_proj" in name or "kv_a_proj_with_mqa" in name
):
cached_a_proj[name] = loaded_weight
q_a_proj_name = (
name
if "q_a_proj" in name
else name.replace("kv_a_proj_with_mqa", "q_a_proj")
)
kv_a_proj_name = (
name
if "kv_a_proj_with_mqa" in name
else name.replace("q_a_proj", "kv_a_proj_with_mqa")
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
futures.append(
executor.submit(
weight_loader,
param,
loaded_weight,
name,
shard_id=shard_id,
expert_id=expert_id,
)
)

# When both q_a_proj and kv_a_proj_with_mqa has been cached, load the fused weight to parameter
if (
q_a_proj_name in cached_a_proj
and kv_a_proj_name in cached_a_proj
break
else:
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
if fuse_qkv_a_proj and (
"q_a_proj" in name or "kv_a_proj_with_mqa" in name
):
q_a_proj_weight = cached_a_proj[q_a_proj_name]
kv_a_proj_weight = cached_a_proj[kv_a_proj_name]
cat_dim = 0
if self.quant_config is not None and (
self.quant_config.get_name() == "awq"
or self.quant_config.get_name() == "moe_wna16"
):
cat_dim = 1
fused_weight = torch.cat(
[q_a_proj_weight, kv_a_proj_weight], dim=cat_dim
)
param_name = (
name.replace("q_a_proj", "fused_qkv_a_proj_with_mqa")
cached_a_proj[name] = loaded_weight
q_a_proj_name = (
name
if "q_a_proj" in name
else name.replace(
"kv_a_proj_with_mqa", "fused_qkv_a_proj_with_mqa"
)
else name.replace("kv_a_proj_with_mqa", "q_a_proj")
)
kv_a_proj_name = (
name
if "kv_a_proj_with_mqa" in name
else name.replace("q_a_proj", "kv_a_proj_with_mqa")
)
param = params_dict[param_name]

# When both q_a_proj and kv_a_proj_with_mqa has been cached, load the fused weight to parameter
if (
q_a_proj_name in cached_a_proj
and kv_a_proj_name in cached_a_proj
):
q_a_proj_weight = cached_a_proj[q_a_proj_name]
kv_a_proj_weight = cached_a_proj[kv_a_proj_name]
cat_dim = 0
if self.quant_config is not None and (
self.quant_config.get_name() == "awq"
or self.quant_config.get_name() == "moe_wna16"
):
cat_dim = 1
fused_weight = torch.cat(
[q_a_proj_weight, kv_a_proj_weight], dim=cat_dim
)
param_name = (
name.replace(
"q_a_proj", "fused_qkv_a_proj_with_mqa"
)
if "q_a_proj" in name
else name.replace(
"kv_a_proj_with_mqa",
"fused_qkv_a_proj_with_mqa",
)
)
param = params_dict[param_name]

weight_loader = getattr(
param, "weight_loader", default_weight_loader
)
futures.append(
executor.submit(weight_loader, param, fused_weight)
)
cached_a_proj.pop(q_a_proj_name)
cached_a_proj.pop(kv_a_proj_name)
else:
if (
"k_scale" in name or "v_scale" in name
) and name not in params_dict:
# modelopt attn kv scale is named differently
for scale in ["k_scale", "v_scale"]:
if scale in name:
name = name.replace(
f"{scale[0]}_proj", "attn_mqa"
)
break
if name not in params_dict:
# modelopt ckpt contains not needed weights for MTP module:
# model.decoder.self_attn.attn_mqa.v_scale and
# model.decoder.self_attn.attn_mqa.k_scale
logger.warning(f"{name} not found in params_dict.")
continue
param = params_dict[name]
weight_loader = getattr(
param, "weight_loader", default_weight_loader
)
weight_loader(param, fused_weight)
cached_a_proj.pop(q_a_proj_name)
cached_a_proj.pop(kv_a_proj_name)
else:
if (
"k_scale" in name or "v_scale" in name
) and name not in params_dict:
# modelopt attn kv scale is named differently
for scale in ["k_scale", "v_scale"]:
if scale in name:
name = name.replace(f"{scale[0]}_proj", "attn_mqa")
break
if name not in params_dict:
# modelopt ckpt contains not needed weights for MTP module:
# model.decoder.self_attn.attn_mqa.v_scale and
# model.decoder.self_attn.attn_mqa.k_scale
logger.warning(f"{name} not found in params_dict.")
continue
param = params_dict[name]
weight_loader = getattr(
param, "weight_loader", default_weight_loader
)
weight_loader(param, loaded_weight)
futures.append(
executor.submit(weight_loader, param, loaded_weight)
)

# Wait for all tasks to complete and raise any exceptions.
for future in concurrent.futures.as_completed(futures):
future.result()

self.post_load_weights(is_nextn=is_nextn, weight_names=weight_names)

Expand Down
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