[Model] Add native OpenPangu Embedded 7B backend

docs/models/supported_models.md

@@ -404,6 +404,7 @@ th {
 | `OPTForCausalLM` | OPT, OPT-IML | `facebook/opt-66b`, `facebook/opt-iml-max-30b`, etc. | ✅︎ | ✅︎ |
 | `OrionForCausalLM` | Orion | `OrionStarAI/Orion-14B-Base`, `OrionStarAI/Orion-14B-Chat`, etc. | | ✅︎ |
 | `OuroForCausalLM` | ouro | `ByteDance/Ouro-1.4B`, `ByteDance/Ouro-2.6B`, etc. | ✅︎ | |
+| `PanguEmbeddedForCausalLM` | openPangu Embedded |`FreedomIntelligence/openPangu-Embedded-7B` | ✅︎ | ✅︎ |
 | `PhiForCausalLM` | Phi | `microsoft/phi-1_5`, `microsoft/phi-2`, etc. | ✅︎ | ✅︎ |
 | `Phi3ForCausalLM` | Phi-4, Phi-3 | `microsoft/Phi-4-mini-instruct`, `microsoft/Phi-4`, `microsoft/Phi-3-mini-4k-instruct`, `microsoft/Phi-3-mini-128k-instruct`, `microsoft/Phi-3-medium-128k-instruct`, etc. | ✅︎ | ✅︎ |
 | `PhiMoEForCausalLM` | Phi-3.5-MoE | `microsoft/Phi-3.5-MoE-instruct`, etc. | ✅︎ | ✅︎ |

tests/models/registry.py

@@ -370,6 +370,9 @@ def check_available_online(
         "OrionStarAI/Orion-14B-Chat", trust_remote_code=True
     ),
     "OuroForCausalLM": _HfExamplesInfo("ByteDance/Ouro-1.4B", trust_remote_code=True),
+    "PanguEmbeddedForCausalLM": _HfExamplesInfo(
+        "FreedomIntelligence/openPangu-Embedded-7B", trust_remote_code=True
+    ),
     "PersimmonForCausalLM": _HfExamplesInfo("adept/persimmon-8b-chat"),
     "PhiForCausalLM": _HfExamplesInfo("microsoft/phi-2"),
     "Phi3ForCausalLM": _HfExamplesInfo("microsoft/Phi-3-mini-4k-instruct"),

vllm/model_executor/models/pangu.py

@@ -0,0 +1,370 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""Native OpenPangu Embedded model implementation."""
+
+from collections.abc import Iterable
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.attention import Attention, AttentionType
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import (
+    get_pp_group,
+    get_tensor_model_parallel_world_size,
+)
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import ColumnParallelLinear, RowParallelLinear
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from vllm.model_executor.models.interfaces import SupportsLoRA, SupportsPP
+from vllm.model_executor.models.llama import LlamaForCausalLM
+from vllm.model_executor.models.utils import (
+    AutoWeightsLoader,
+    PPMissingLayer,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+)
+from vllm.sequence import IntermediateTensors
+
+
+class PanguMLP(nn.Module):
+    """Feed-forward network for PanguEmbedded layers."""
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        *,
+        bias: bool,
+        quant_config: QuantizationConfig | None,
+        prefix: str,
+    ) -> None:
+        super().__init__()
+        self.gate_proj = ColumnParallelLinear(
+            hidden_size,
+            intermediate_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_proj",
+        )
+        self.up_proj = ColumnParallelLinear(
+            hidden_size,
+            intermediate_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
+        )
+        self.act_fn = get_act_fn(hidden_act)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        gate, _ = self.gate_proj(hidden_states)
+        up, _ = self.up_proj(hidden_states)
+        hidden_states = self.act_fn(gate) * up
+        hidden_states, _ = self.down_proj(hidden_states)
+        return hidden_states
+
+
+class PanguAttention(nn.Module):
+    """Self-attention block with GQA."""
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        *,
+        cache_config: CacheConfig | None,
+        quant_config: QuantizationConfig | None,
+        prefix: str,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.total_num_heads = config.num_attention_heads
+        self.total_num_kv_heads = getattr(
+            config, "num_key_value_heads", config.num_attention_heads
+        )
+        tp_size = get_tensor_model_parallel_world_size()
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        if self.total_num_kv_heads >= tp_size:
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            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 = getattr(
+            config,
+            "head_dim",
+            self.hidden_size // self.total_num_heads,
+        )
+        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
+
+        rope_theta = getattr(config, "rope_theta", 10000.0)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        if rope_scaling is not None:
+            rope_scaling = dict(rope_scaling)
+            original_max_position = getattr(
+                config, "original_max_position_embeddings", None
+            )
+            if original_max_position is not None:
+                rope_scaling.setdefault(
+                    "original_max_position_embeddings", original_max_position
+                )
+        max_position_embeddings = getattr(config, "max_position_embeddings", 2048)
+
+        bias = getattr(config, "bias", False)
+        self.q_proj = ColumnParallelLinear(
+            self.hidden_size,
+            self.total_num_heads * self.head_dim,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.q_proj",
+        )
+        self.k_proj = ColumnParallelLinear(
+            self.hidden_size,
+            self.total_num_kv_heads * self.head_dim,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.k_proj",
+        )
+        self.v_proj = ColumnParallelLinear(
+            self.hidden_size,
+            self.total_num_kv_heads * self.head_dim,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.v_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            self.hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+            is_neox_style=True,
+        )
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            attn_type=AttentionType.DECODER,
+            prefix=f"{prefix}.attn",
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        q, _ = self.q_proj(hidden_states)
+        k, _ = self.k_proj(hidden_states)
+        v, _ = self.v_proj(hidden_states)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class PanguDecoderLayer(nn.Module):
+    """Single decoder block for PanguEmbedded."""
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        config: PretrainedConfig | None = None,
+    ) -> None:
+        super().__init__()
+        config = config or vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = self.get_quant_config(vllm_config)
+
+        self.hidden_size = config.hidden_size
+        self.self_attn = PanguAttention(
+            config,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.mlp = PanguMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            bias=getattr(config, "bias", False),
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+        self.input_layernorm = RMSNorm(
+            config.hidden_size, eps=getattr(config, "rms_norm_eps", 1e-5)
+        )
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, eps=getattr(config, "rms_norm_eps", 1e-5)
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(hidden_states, residual)
+
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+        )
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+    def get_quant_config(self, vllm_config: VllmConfig) -> QuantizationConfig | None:
+        return vllm_config.quant_config
+
+
+class PanguModel(nn.Module):
+    """Backbone model for OpenPangu Embedded."""
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        layer_type: type[nn.Module] = PanguDecoderLayer,
+    ) -> None:
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.quant_config = quant_config
+        lora_vocab = (
+            (lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
+            if lora_config
+            else 0
+        )
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.org_vocab_size = config.vocab_size
+        if get_pp_group().is_first_rank or (
+            getattr(config, "tie_word_embeddings", True) and get_pp_group().is_last_rank
+        ):
+            self.embed_tokens = VocabParallelEmbedding(
+                self.vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                quant_config=quant_config,
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: layer_type(vllm_config=vllm_config, prefix=prefix),
+            prefix=f"{prefix}.layers",
+        )
+
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(
+                config.hidden_size, eps=getattr(config, "rms_norm_eps", 1e-5)
+            )
+        else:
+            self.norm = PPMissingLayer()
+
+        self.aux_hidden_state_layers: tuple[int, ...] = ()
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states", "residual"], config.hidden_size
+        )
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors | tuple[torch.Tensor, list[torch.Tensor]]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        aux_hidden_states: list[torch.Tensor] = []
+        for idx, layer in enumerate(self.layers[self.start_layer : self.end_layer]):
+            if residual is None:
+                aux_hidden_states.append(hidden_states)
+            else:
+                aux_hidden_states.append(hidden_states + residual)
+            hidden_states, residual = layer(positions, hidden_states, residual)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors(
+                {"hidden_states": hidden_states, "residual": residual}
+            )
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+
+        if aux_hidden_states:
+            return hidden_states, aux_hidden_states
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
+
+
+class PanguForCausalLM(LlamaForCausalLM, SupportsLoRA, SupportsPP):
+    """Causal LM head for OpenPangu Embedded."""
+
+    packed_modules_mapping: dict[str, list[str]] = {}
+    mistral_mapping: dict[str, str] = {}
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(
+            vllm_config=vllm_config,
+            prefix=prefix,
+            layer_type=PanguDecoderLayer,
+        )
+
+    def _init_model(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        layer_type: type[nn.Module] = PanguDecoderLayer,
+    ):
+        return PanguModel(vllm_config=vllm_config, prefix=prefix, layer_type=layer_type)

vllm/model_executor/models/registry.py

@@ -149,6 +149,7 @@
     "OPTForCausalLM": ("opt", "OPTForCausalLM"),
     "OrionForCausalLM": ("orion", "OrionForCausalLM"),
     "OuroForCausalLM": ("ouro", "OuroForCausalLM"),
+    "PanguEmbeddedForCausalLM": ("pangu", "PanguForCausalLM"),
     "PersimmonForCausalLM": ("persimmon", "PersimmonForCausalLM"),
     "PhiForCausalLM": ("phi", "PhiForCausalLM"),
     "Phi3ForCausalLM": ("phi3", "Phi3ForCausalLM"),