[Model] use AutoWeightsLoader for olmoe,opt,orion,persimmon,phi3_small (#16548)

Signed-off-by: rongfu.leng <rongfu.leng@daocloud.io>

vllm/model_executor/models/olmoe.py

@@ -39,7 +39,7 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsPP
-from .utils import (is_pp_missing_parameter,
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
 
@@ -255,7 +255,7 @@ class OlmoeModel(nn.Module):
         quant_config = vllm_config.quant_config
 
         self.vocab_size = config.vocab_size
-
+        self.config = config
         self.embed_tokens = VocabParallelEmbedding(
             config.vocab_size,
             config.hidden_size,
@@ -308,56 +308,6 @@ class OlmoeModel(nn.Module):
         hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 
-
-class OlmoeForCausalLM(nn.Module, SupportsPP):
-
-    fall_back_to_pt_during_load = False
-
-    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
-        super().__init__()
-        config = vllm_config.model_config.hf_config
-        quant_config = vllm_config.quant_config
-        self.config = config
-        self.quant_config = quant_config
-        self.model = OlmoeModel(vllm_config=vllm_config,
-                                prefix=maybe_prefix(prefix, "model"))
-        self.lm_head = ParallelLMHead(config.vocab_size,
-                                      config.hidden_size,
-                                      quant_config=quant_config)
-        self.logits_processor = LogitsProcessor(config.vocab_size)
-        self.sampler = get_sampler()
-
-        self.make_empty_intermediate_tensors = (
-            self.model.make_empty_intermediate_tensors)
-
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.model.get_input_embeddings(input_ids)
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-    ) -> Union[torch.Tensor, IntermediateTensors]:
-        hidden_states = self.model(input_ids, positions, intermediate_tensors,
-                                   inputs_embeds)
-        return hidden_states
-
-    def compute_logits(self, hidden_states: torch.Tensor,
-                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
-        logits = self.logits_processor(self.lm_head, hidden_states,
-                                       sampling_metadata)
-        return logits
-
-    def sample(
-        self,
-        logits: Optional[torch.Tensor],
-        sampling_metadata: SamplingMetadata,
-    ) -> Optional[SamplerOutput]:
-        next_tokens = self.sampler(logits, sampling_metadata)
-        return next_tokens
-
     def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
         stacked_params_mapping = [
@@ -380,8 +330,6 @@ class OlmoeForCausalLM(nn.Module, SupportsPP):
         params_dict = dict(self.named_parameters())
         loaded_params: Set[str] = set()
         for name, loaded_weight in weights:
-            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:
@@ -453,3 +401,59 @@ class OlmoeForCausalLM(nn.Module, SupportsPP):
                     weight_loader(param, loaded_weight)
             loaded_params.add(name)
         return loaded_params
+
+
+class OlmoeForCausalLM(nn.Module, SupportsPP):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.quant_config = quant_config
+        self.model = OlmoeModel(vllm_config=vllm_config,
+                                prefix=maybe_prefix(prefix, "model"))
+        self.lm_head = ParallelLMHead(config.vocab_size,
+                                      config.hidden_size,
+                                      quant_config=quant_config)
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.sampler = get_sampler()
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        hidden_states = self.model(input_ids, positions, intermediate_tensors,
+                                   inputs_embeds)
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def sample(
+        self,
+        logits: Optional[torch.Tensor],
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(logits, sampling_metadata)
+        return next_tokens
+
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=["rotary_emb.inv_freq"],
+        )
+        return loader.load_weights(weights)

vllm/model_executor/models/opt.py

@@ -43,7 +43,7 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsPP
-from .utils import (is_pp_missing_parameter,
+from .utils import (AutoWeightsLoader, WeightsMapper, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
 
@@ -313,6 +313,43 @@ class OPTModel(nn.Module):
                             intermediate_tensors,
                             inputs_embeds=inputs_embeds)
 
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: Set[str] = set()
+        for name, loaded_weight in weights:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    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
+                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
+                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)
+            loaded_params.add(name)
+        return loaded_params
+
 
 class OPTForCausalLM(nn.Module, SupportsPP):
     packed_modules_mapping = {
@@ -320,6 +357,10 @@ class OPTForCausalLM(nn.Module, SupportsPP):
         "gate_up_proj": ["gate_proj", "up_proj"]
     }
 
+    hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={
+        "decoder.": "model.decoder.",
+    })
+
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
@@ -371,42 +412,9 @@ class OPTForCausalLM(nn.Module, SupportsPP):
 
     def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            ("qkv_proj", "q_proj", "q"),
-            ("qkv_proj", "k_proj", "k"),
-            ("qkv_proj", "v_proj", "v"),
-        ]
-        params_dict = dict(self.named_parameters(remove_duplicate=False))
-        loaded_params: Set[str] = set()
-        for name, loaded_weight in weights:
-            if "lm_head.weight" in name and self.config.tie_word_embeddings:
-                continue
-            if name.startswith("decoder."):
-                name = "model." + name
-
-            for (param_name, weight_name, shard_id) in stacked_params_mapping:
-                if weight_name not in name:
-                    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
-                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
-                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)
-            loaded_params.add(name)
-        return loaded_params
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head.weight"]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

vllm/model_executor/models/orion.py

@@ -30,7 +30,7 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsPP
-from .utils import (is_pp_missing_parameter,
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
 
@@ -260,6 +260,45 @@ class OrionModel(nn.Module):
         hidden_states = self.norm(hidden_states)
         return hidden_states
 
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: Set[str] = set()
+        for name, loaded_weight in weights:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    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
+                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
+                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)
+            loaded_params.add(name)
+        return loaded_params
+
 
 class OrionForCausalLM(nn.Module, SupportsPP):
 
@@ -314,46 +353,14 @@ class OrionForCausalLM(nn.Module, SupportsPP):
 
     def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            ("qkv_proj", "q_proj", "q"),
-            ("qkv_proj", "k_proj", "k"),
-            ("qkv_proj", "v_proj", "v"),
-            ("gate_up_proj", "gate_proj", 0),
-            ("gate_up_proj", "up_proj", 1),
-        ]
-        params_dict = dict(self.named_parameters())
-        loaded_params: Set[str] = set()
-        for name, loaded_weight in weights:
-            if "rotary_emb.inv_freq" in name:
-                continue
-            if ("rotary_emb.cos_cached" in name
-                    or "rotary_emb.sin_cached" in name):
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=([
+                "rotary_emb.inv_freq",
                 # Models trained using ColossalAI may include these tensors in
                 # the checkpoint. Skip them.
-                continue
-            for (param_name, weight_name, shard_id) in stacked_params_mapping:
-                if weight_name not in name:
-                    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
-                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
-                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)
-            loaded_params.add(name)
-        return loaded_params
+                "rotary_emb.cos_cached",
+                "rotary_emb.sin_cached"
+            ]),
+        )
+        return loader.load_weights(weights)

vllm/model_executor/models/persimmon.py

@@ -46,7 +46,7 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsPP
-from .utils import (is_pp_missing_parameter,
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
 
@@ -221,7 +221,7 @@ class PersimmonModel(nn.Module):
         quant_config = vllm_config.quant_config
 
         self.vocab_size = config.vocab_size
-
+        self.config = config
         self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
                                                    config.hidden_size)
         self.start_layer, self.end_layer, self.layers = make_layers(
@@ -260,6 +260,38 @@ class PersimmonModel(nn.Module):
         hidden_states = self.final_layernorm(hidden_states)
         return hidden_states
 
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: Set[str] = set()
+        for name, loaded_weight in weights:
+            if is_pp_missing_parameter(name, self):
+                continue
+            param = params_dict[name]
+
+            if "query_key_value" in name:
+                # copy from vllm/model_executor/models/bloom.py
+                # NOTE: Persimmon's fused QKV's output_dim has the shape of
+                # (num_heads * 3 * head_size), while the
+                # required shape is (3 * num_heads * head_size).
+                # Thus, we need weight conversion.
+                output_dim = getattr(param, "output_dim", None)
+                num_heads = self.config.num_attention_heads
+                if output_dim is not None:
+                    loaded_weight_shape = loaded_weight.shape
+                    loaded_weight = loaded_weight.view(
+                        loaded_weight_shape[:output_dim] + (num_heads, 3, -1) +
+                        loaded_weight_shape[output_dim + 1:])
+                    loaded_weight = loaded_weight.transpose(
+                        output_dim, output_dim + 1)
+                    loaded_weight = loaded_weight.reshape(loaded_weight_shape)
+
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
 
 class PersimmonForCausalLM(nn.Module, SupportsPP):
 
@@ -315,39 +347,5 @@ class PersimmonForCausalLM(nn.Module, SupportsPP):
 
     def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
-        params_dict = dict(self.named_parameters(remove_duplicate=False))
-        loaded_params: Set[str] = set()
-        for name, loaded_weight in weights:
-            if "rotary_emb.inv_freq" in name:
-                continue
-            if ("rotary_emb.cos_cached" in name
-                    or "rotary_emb.sin_cached" in name):
-                # Models trained using ColossalAI may include these tensors in
-                # the checkpoint. Skip them.
-                continue
-            if is_pp_missing_parameter(name, self):
-                continue
-            param = params_dict[name]
-
-            if "query_key_value" in name:
-                # copy from vllm/model_executor/models/bloom.py
-                # NOTE: Persimmon's fused QKV's output_dim has the shape of
-                # (num_heads * 3 * head_size), while the
-                # required shape is (3 * num_heads * head_size).
-                # Thus, we need weight conversion.
-                output_dim = getattr(param, "output_dim", None)
-                num_heads = self.config.num_attention_heads
-                if output_dim is not None:
-                    loaded_weight_shape = loaded_weight.shape
-                    loaded_weight = loaded_weight.view(
-                        loaded_weight_shape[:output_dim] + (num_heads, 3, -1) +
-                        loaded_weight_shape[output_dim + 1:])
-                    loaded_weight = loaded_weight.transpose(
-                        output_dim, output_dim + 1)
-                    loaded_weight = loaded_weight.reshape(loaded_weight_shape)
-
-            weight_loader = getattr(param, "weight_loader",
-                                    default_weight_loader)
-            weight_loader(param, loaded_weight)
-            loaded_params.add(name)
-        return loaded_params
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)

vllm/model_executor/models/phi3_small.py

@@ -26,7 +26,7 @@ from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsPP
-from .utils import (is_pp_missing_parameter,
+from .utils import (AutoWeightsLoader, WeightsMapper, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
 
@@ -353,10 +353,29 @@ class Phi3SmallModel(nn.Module):
         hidden_states = self.final_layernorm(hidden_states)
         return hidden_states
 
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        params_dict = dict(self.named_parameters())
+        loaded_params: Set[str] = set()
+        for name, loaded_weight in weights:
+            if name.endswith(".bias") 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)
+            loaded_params.add(name)
+        return loaded_params
+
 
 class Phi3SmallForCausalLM(nn.Module, SupportsPP):
     _tied_weights_keys = ["lm_head.weight"]
 
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_suffix={"rotary_emb.inv_freq": None})
+
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
@@ -448,21 +467,8 @@ class Phi3SmallForCausalLM(nn.Module, SupportsPP):
 
     def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
-
-        params_dict = dict(self.named_parameters())
-        loaded_params: Set[str] = set()
-        for name, loaded_weight in weights:
-            if "rotary_emb.inv_freq" in name:
-                continue
-            if name.endswith(".bias") and name not in params_dict:
-                continue
-            if is_pp_missing_parameter(name, self):
-                continue
-            if "lm_head.weight" in name and self.config.tie_word_embeddings:
-                continue
-            param = params_dict[name]
-            weight_loader = getattr(param, "weight_loader",
-                                    default_weight_loader)
-            weight_loader(param, loaded_weight)
-            loaded_params.add(name)
-        return loaded_params
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head.weight"]
+                           if self.config.tie_word_embeddings else None))
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)