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Revert "[FEAT] Support GGUF format" (#2285)

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This commit is contained in:
Lianmin Zheng
2024-11-30 19:03:26 -08:00
committed by GitHub
parent d622851dc9
commit 7e4c6dd8da
39 changed files with 89 additions and 180 deletions
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11
python/sglang/srt/models/baichuan.py
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@@ -338,12 +338,11 @@ class BaiChuanBaseForCausalLM(nn.Module):
self.quant_config = quant_config
self.model = BaiChuanModel(config, position_embedding, quant_config)
self.lm_head = ParallelLMHead(
config.vocab_size, config.hidden_size, quant_config=quant_config
)
if self.config.tie_word_embeddings:
self.lm_head = self.model.embed_tokens
else:
self.lm_head = ParallelLMHead(
config.vocab_size, config.hidden_size, quant_config=quant_config
)
self.lm_head.weight = self.model.embed_tokens.weight
self.logits_processor = LogitsProcessor(config)
def forward(
@@ -354,7 +353,7 @@ class BaiChuanBaseForCausalLM(nn.Module):
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, forward_batch)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

2
python/sglang/srt/models/chatglm.py
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@@ -378,7 +378,7 @@ class ChatGLMForCausalLM(nn.Module):
) -> torch.Tensor:
hidden_states = self.transformer(input_ids, positions, forward_batch)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

2
python/sglang/srt/models/commandr.py
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@@ -339,7 +339,7 @@ class CohereForCausalLM(nn.Module):
forward_batch,
)
return self.logits_processor(
input_ids, hidden_states, self.model.embed_tokens, forward_batch
input_ids, hidden_states, self.model.embed_tokens.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

2
python/sglang/srt/models/dbrx.py
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@@ -390,7 +390,7 @@ class DbrxForCausalLM(nn.Module):
) -> torch.Tensor:
hidden_states = self.transformer(input_ids, positions, forward_batch)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

2
python/sglang/srt/models/deepseek.py
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@@ -394,7 +394,7 @@ class DeepseekForCausalLM(nn.Module):
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, forward_batch)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

2
python/sglang/srt/models/deepseek_v2.py
View File

@@ -763,7 +763,7 @@ class DeepseekV2ForCausalLM(nn.Module):
hidden_states = self.model(input_ids, positions, forward_batch)
if not forward_batch.forward_mode.is_idle():
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

2
python/sglang/srt/models/exaone.py
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@@ -314,7 +314,7 @@ class ExaoneForCausalLM(nn.Module):
input_ids, positions, forward_batch, input_embeds
)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

2
python/sglang/srt/models/gemma.py
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@@ -298,7 +298,7 @@ class GemmaForCausalLM(nn.Module):
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
return self.logits_processor(
input_ids, hidden_states, self.model.embed_tokens, forward_batch
input_ids, hidden_states, self.model.embed_tokens.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

2
python/sglang/srt/models/gemma2.py
View File

@@ -363,7 +363,7 @@ class Gemma2ForCausalLM(nn.Module):
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
return self.logits_processor(
input_ids, hidden_states, self.model.embed_tokens, forward_batch
input_ids, hidden_states, self.model.embed_tokens.weight, forward_batch
)
def get_attention_sliding_window_size(self):

2
python/sglang/srt/models/gpt2.py
View File

@@ -247,7 +247,7 @@ class GPT2LMHeadModel(nn.Module):
) -> torch.Tensor:
hidden_states = self.transformer(input_ids, positions, forward_batch)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

2
python/sglang/srt/models/gpt_bigcode.py
View File

@@ -271,7 +271,7 @@ class GPTBigCodeForCausalLM(nn.Module):
) -> torch.Tensor:
hidden_states = self.transformer(input_ids, positions, forward_batch)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

2
python/sglang/srt/models/grok.py
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@@ -304,7 +304,7 @@ class Grok1ForCausalLM(nn.Module):
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

2
python/sglang/srt/models/internlm2.py
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@@ -270,7 +270,7 @@ class InternLM2ForCausalLM(nn.Module):
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
return self.logits_processor(
input_ids, hidden_states, self.output, forward_batch
input_ids, hidden_states, self.output.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

27
python/sglang/srt/models/llama.py
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@@ -258,7 +258,6 @@ class LlamaModel(nn.Module):
self.embed_tokens = VocabParallelEmbedding(
config.vocab_size,
config.hidden_size,
quant_config=quant_config,
)
self.layers = make_layers(
config.num_hidden_layers,
@@ -306,12 +305,7 @@ class LlamaForCausalLM(nn.Module):
self.quant_config = quant_config
self.torchao_config = global_server_args_dict["torchao_config"]
self.model = LlamaModel(config, quant_config=quant_config)
if self.config.tie_word_embeddings:
self.lm_head = self.model.embed_tokens
else:
self.lm_head = ParallelLMHead(
config.vocab_size, config.hidden_size, quant_config=quant_config
)
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
self.logits_processor = LogitsProcessor(config)
self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
self.stacked_params_mapping = [
@@ -335,7 +329,7 @@ class LlamaForCausalLM(nn.Module):
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
if not get_embedding:
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
else:
return self.pooler(hidden_states, forward_batch)
@@ -379,6 +373,7 @@ class LlamaForCausalLM(nn.Module):
return len(params_dict)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
embed_tokens_weight = None
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
(".qkv_proj", ".q_proj", "q"),
@@ -390,6 +385,12 @@ class LlamaForCausalLM(nn.Module):
params_dict = dict(self.named_parameters())
load_tie_word_embeddings = (
hasattr(self.config, "tie_word_embeddings")
and self.config.tie_word_embeddings
and "lm_head.weight" in params_dict
)
for name, loaded_weight in weights:
if "rotary_emb.inv_freq" in name or "projector" in name:
continue
@@ -422,6 +423,16 @@ class LlamaForCausalLM(nn.Module):
weight_loader = getattr(param, "weight_loader", default_weight_loader)
weight_loader(param, loaded_weight)
if load_tie_word_embeddings and name == "model.embed_tokens.weight":
embed_tokens_weight = loaded_weight
if load_tie_word_embeddings:
# Tie output embedding layer to input embedding layer, to solve issues where lm_head.weight is missing
param = self.lm_head.weight
weight_loader = getattr(param, "weight_loader", default_weight_loader)
if embed_tokens_weight is not None:
weight_loader(param, embed_tokens_weight)
apply_torchao_config_(self, params_dict, set(["proj.weight"]))
def get_weights_by_name(

8
python/sglang/srt/models/minicpm.py
View File

@@ -308,10 +308,12 @@ class MiniCPMForCausalLM(nn.Module):
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
hidden_states = hidden_states / self.scale_width
if self.config.tie_word_embeddings:
lm_head = self.model.embed_tokens
lm_head_weight = self.model.embed_tokens.weight
else:
lm_head = self.lm_head
return self.logits_processor(input_ids, hidden_states, lm_head, forward_batch)
lm_head_weight = self.lm_head.weight
return self.logits_processor(
input_ids, hidden_states, lm_head_weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
stacked_params_mapping = [

8
python/sglang/srt/models/minicpm3.py
View File

@@ -585,10 +585,12 @@ class MiniCPM3ForCausalLM(nn.Module):
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
hidden_states = hidden_states / self.scale_width
if self.config.tie_word_embeddings:
lm_head = self.model.embed_tokens
lm_head_weight = self.model.embed_tokens.weight
else:
lm_head = self.lm_head
return self.logits_processor(input_ids, hidden_states, lm_head, forward_batch)
lm_head_weight = self.lm_head.weight
return self.logits_processor(
input_ids, hidden_states, lm_head_weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
stacked_params_mapping = [

2
python/sglang/srt/models/mixtral.py
View File

@@ -310,7 +310,7 @@ class MixtralForCausalLM(nn.Module):
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

2
python/sglang/srt/models/mixtral_quant.py
View File

@@ -343,7 +343,7 @@ class QuantMixtralForCausalLM(nn.Module):
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

2
python/sglang/srt/models/mllama.py
View File

@@ -966,7 +966,7 @@ class MllamaForConditionalGeneration(nn.Module):
skip_cross_attention=skip_cross_attention,
)
return self.logits_processor(
input_ids, hidden_states, self.language_model.lm_head, forward_batch
input_ids, hidden_states, self.language_model.lm_head.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

7
python/sglang/srt/models/olmo.py
View File

@@ -306,7 +306,7 @@ class OlmoForCausalLM(nn.Module):
input_embeds=input_embeds,
)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
@@ -326,6 +326,11 @@ class OlmoForCausalLM(nn.Module):
# Models trained using ColossalAI may include these tensors in
# the checkpoint. Skip them.
continue
# With tie_word_embeddings, we can skip lm_head.weight
# The weight might appear unnecessarily in the files if the model is
# processed with quantization, LoRA, fine-tuning, etc.
if self.config.tie_word_embeddings and "lm_head.weight" in name:
continue
for param_name, weight_name, shard_id in stacked_params_mapping:
if weight_name not in name:
continue

2
python/sglang/srt/models/olmoe.py
View File

@@ -321,7 +321,7 @@ class OlmoeForCausalLM(nn.Module):
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

7
python/sglang/srt/models/phi3_small.py
View File

@@ -397,13 +397,10 @@ class Phi3SmallForCausalLM(nn.Module):
def compute_logits(
self,
input_ids: torch.LongTensor,
hidden_states: torch.Tensor,
sampling_metadata,
) -> Optional[torch.Tensor]:
logits = self.logits_processor(
input_ids, self.lm_head, hidden_states, sampling_metadata
)
logits = self.logits_processor(self.lm_head, hidden_states, sampling_metadata)
if self.dummy_token_indices is not None and logits is not None:
logits.index_fill_(-1, self.dummy_token_indices, -torch.inf)
return logits
@@ -425,7 +422,7 @@ class Phi3SmallForCausalLM(nn.Module):
if not get_embedding:
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
else:

2
python/sglang/srt/models/qwen.py
View File

@@ -260,7 +260,7 @@ class QWenLMHeadModel(nn.Module):
):
hidden_states = self.transformer(input_ids, positions, forward_batch)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

16
python/sglang/srt/models/qwen2.py
View File

@@ -230,7 +230,6 @@ class Qwen2Model(nn.Module):
self.embed_tokens = VocabParallelEmbedding(
config.vocab_size,
config.hidden_size,
quant_config=quant_config,
)
self.layers = make_layers(
config.num_hidden_layers,
@@ -277,12 +276,7 @@ class Qwen2ForCausalLM(nn.Module):
self.config = config
self.quant_config = quant_config
self.model = Qwen2Model(config, quant_config=quant_config)
if config.tie_word_embeddings:
self.lm_head = self.model.embed_tokens
else:
self.lm_head = ParallelLMHead(
config.vocab_size, config.hidden_size, quant_config=quant_config
)
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
self.logits_processor = LogitsProcessor(config)
self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
@@ -298,7 +292,7 @@ class Qwen2ForCausalLM(nn.Module):
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
if not get_embedding:
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
else:
return self.pooler(hidden_states, forward_batch)
@@ -312,7 +306,6 @@ class Qwen2ForCausalLM(nn.Module):
("gate_up_proj", "gate_proj", 0),
("gate_up_proj", "up_proj", 1),
]
params_dict = dict(self.named_parameters())
for name, loaded_weight in weights:
if "rotary_emb.inv_freq" in name or "projector" in name:
@@ -342,6 +335,11 @@ class Qwen2ForCausalLM(nn.Module):
param = params_dict[name]
weight_loader = getattr(param, "weight_loader", default_weight_loader)
weight_loader(param, loaded_weight)
if (
self.config.tie_word_embeddings
and name == "model.embed_tokens.weight"
):
weight_loader(params_dict["lm_head.weight"], loaded_weight)
EntryClass = Qwen2ForCausalLM

2
python/sglang/srt/models/qwen2_moe.py
View File

@@ -376,7 +376,7 @@ class Qwen2MoeForCausalLM(nn.Module):
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

4
python/sglang/srt/models/qwen2_vl.py
View File

@@ -668,7 +668,7 @@ class Qwen2VLForConditionalGeneration(nn.Module):
if not get_embedding:
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
else:
return self.pooler(hidden_states, forward_batch)
@@ -686,6 +686,8 @@ class Qwen2VLForConditionalGeneration(nn.Module):
for name, loaded_weight in weights:
if "rotary_emb.inv_freq" in name:
continue
if self.config.tie_word_embeddings and "lm_head.weight" in name:
continue
for param_name, weight_name, shard_id in stacked_params_mapping:
if weight_name not in name:
continue

2
python/sglang/srt/models/stablelm.py
View File

@@ -261,7 +261,7 @@ class StableLmForCausalLM(nn.Module):
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):

15
python/sglang/srt/models/torch_native_llama.py
View File

@@ -396,10 +396,7 @@ class TorchNativeLlamaForCausalLM(nn.Module):
self.torchao_config = global_server_args_dict["torchao_config"]
self.supports_torch_tp = True
self.model = LlamaModel(config, quant_config=quant_config)
if self.config.tie_word_embeddings:
self.lm_head = self.model.embed_tokens
else:
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
self.logits_processor = LogitsProcessor(config)
# turning off autotune for fp8dq since it doesn't give speedup and
@@ -416,7 +413,7 @@ class TorchNativeLlamaForCausalLM(nn.Module):
) -> LogitsProcessorOutput:
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def get_hidden_dim(self, module_name):
@@ -504,6 +501,14 @@ class TorchNativeLlamaForCausalLM(nn.Module):
weight_loader = getattr(param, "weight_loader", default_weight_loader)
weight_loader(param, loaded_weight)
if (
hasattr(self.config, "tie_word_embeddings")
and self.config.tie_word_embeddings
):
# Tie output embedding layer to input embedding layer, to solve issues where lm_head.weight is missing
param = self.lm_head.weight
weight_loader = getattr(param, "weight_loader", default_weight_loader)
weight_loader(param, self.model.embed_tokens.weight)
apply_torchao_config_(self, params_dict, set(["proj.weight"]))

2
python/sglang/srt/models/xverse.py
View File

@@ -315,7 +315,7 @@ class XverseForCausalLM(nn.Module):
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def load_weights(

2
python/sglang/srt/models/xverse_moe.py
View File

@@ -390,7 +390,7 @@ class XverseMoeForCausalLM(nn.Module):
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, forward_batch)
return self.logits_processor(
input_ids, hidden_states, self.lm_head, forward_batch
input_ids, hidden_states, self.lm_head.weight, forward_batch
)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):