add qwen3

vllm-v0.6.2/vllm/model_executor/models/florence2.py

@@ -0,0 +1,257 @@
+import math
+from typing import Iterable, List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+
+from vllm.attention import AttentionMetadata
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.bart import (BartDecoder, BartEncoder,
+                                             BartParallelLMHead,
+                                             BartScaledWordEmbedding)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .utils import AutoWeightsLoader
+
+
+class Florence2LanguageModel(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.config = config
+
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.shared = BartScaledWordEmbedding(self.vocab_size, config.d_model)
+        self.encoder = BartEncoder(config,
+                                   cache_config=cache_config,
+                                   quant_config=quant_config)
+        self.decoder = BartDecoder(config,
+                                   cache_config=cache_config,
+                                   quant_config=quant_config)
+
+        if self.config.tie_word_embeddings:
+            self.encoder.embed_tokens.weight = self.shared.weight
+            self.decoder.embed_tokens.weight = self.shared.weight
+
+    def forward(self, input_ids: torch.Tensor, positions: torch.Tensor,
+                encoder_input_ids: torch.Tensor,
+                encoder_positions: torch.Tensor, kv_caches: List[torch.Tensor],
+                attn_metadata: AttentionMetadata) -> torch.Tensor:
+        r"""
+        Args:
+            input_ids
+                Indices of *decoder* input sequence tokens in the vocabulary.
+                Padding will be ignored by default should you
+                provide it.
+            positions
+                Positions of *decoder* input sequence tokens.
+            encoder_input_ids
+                Indices of *encoder* input sequence tokens in the vocabulary.
+            encoder_positions:
+                Positions of *encoder* input sequence tokens.
+            kv_caches:
+                Layer-wise list of KV cache tensors
+            attn_metadata:
+                vLLM Attention metadata structure
+        Returns:
+            Model output torch.Tensor
+        """
+
+        encoder_hidden_states = None
+
+        if encoder_input_ids.numel() > 0:
+            # Run encoder attention if a non-zero number of encoder tokens
+            # are provided as input
+            encoder_hidden_states = self.encoder(input_ids=encoder_input_ids,
+                                                 positions=encoder_positions,
+                                                 kv_caches=kv_caches,
+                                                 attn_metadata=attn_metadata)
+
+        # decoder outputs consists of
+        # (dec_features, past_key_value, dec_hidden, dec_attn)
+        decoder_outputs = self.decoder(
+            decoder_input_ids=input_ids,
+            decoder_positions=positions,
+            encoder_hidden_states=encoder_hidden_states,
+            kv_caches=kv_caches,
+            attn_metadata=attn_metadata)
+
+        return decoder_outputs
+
+
+class Florence2LanguageForConditionalGeneration(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+
+        self.config = config
+        self.model = Florence2LanguageModel(vllm_config=vllm_config,
+                                            prefix=prefix)
+        embed_scale = math.sqrt(
+            config.d_model) if config.scale_embedding else 1.0
+
+        self.vocab_size = config.vocab_size
+        self.lm_head = BartParallelLMHead(self.vocab_size,
+                                          config.d_model,
+                                          embed_scale=embed_scale)
+
+        self.logits_processor = LogitsProcessor(self.vocab_size,
+                                                config.vocab_size)
+        self.sampler = get_sampler()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        encoder_input_ids: torch.Tensor,
+        encoder_positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        **kwargs,
+    ) -> torch.Tensor:
+        r"""
+        Args:
+            input_ids
+                torch.Tensor of *decoder* input token ids.
+            positions
+                torch.Tensor of *decoder* position indices.
+            encoder_input_ids
+                torch.Tensor of *encoder* input token ids.
+            encoder_positions
+                torch.Tensor of *encoder* position indices
+            kv_caches:
+                Layer-wise list of KV cache tensors
+            attn_metadata:
+                vLLM Attention metadata structure
+        Returns:
+            Output torch.Tensor
+        """
+        return self.model(input_ids, positions, encoder_input_ids,
+                          encoder_positions, kv_caches, attn_metadata)
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def sample(self, logits: torch.Tensor,
+               sampling_metadata: SamplingMetadata) -> SamplerOutput:
+        next_tokens = self.sampler(logits, sampling_metadata)
+        return next_tokens
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        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())
+        for name, loaded_weight in weights:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+
+                param = params_dict[name.replace(weight_name, param_name)]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                if "final_logits_bias" in name:
+                    continue
+                if self.config.tie_word_embeddings and "embed_tokens" in name:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+
+class Florence2ForConditionalGeneration(nn.Module):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+
+        # TODO(Isotr0py): Add vision backbone
+        self.language_model = Florence2LanguageForConditionalGeneration(
+            vllm_config=vllm_config.with_hf_config(config.text_config),
+            prefix=prefix,
+        )
+
+    @property
+    def sampler(self):
+        return self.language_model.sampler
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        *,
+        encoder_input_ids: torch.Tensor,
+        encoder_positions: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        r"""
+        Args:
+            input_ids
+                torch.Tensor of *decoder* input token ids.
+            positions
+                torch.Tensor of *decoder* position indices.
+            encoder_input_ids
+                torch.Tensor of *encoder* input token ids.
+            encoder_positions
+                torch.Tensor of *encoder* position indices
+            kv_caches:
+                Layer-wise list of KV cache tensors
+            attn_metadata:
+                vLLM Attention metadata structure
+        Returns:
+            Output torch.Tensor
+        """
+        return self.language_model(input_ids, positions, encoder_input_ids,
+                                   encoder_positions, kv_caches, attn_metadata)
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> SamplerOutput:
+        return self.language_model.sample(logits, sampling_metadata)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        skip_prefixes = [
+            'image_projection', "vision_tower", "image_proj_norm",
+            "image_pos_embed", "visual_temporal_embed"
+        ]
+        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
+        loader.load_weights(weights)