Sync from v0.13

vllm/model_executor/models/internlm2.py

@@ -1,50 +1,81 @@
-# -*- coding: utf-8 -*-
-from typing import Any, Dict, Iterable, List, Optional, Tuple
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Iterable
+from functools import partial
+from itertools import islice
+from typing import Any
 
 import torch
 from torch import nn
 from transformers import PretrainedConfig
 
-from vllm.attention import Attention, AttentionMetadata
-from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.attention.layer import Attention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import (
+    get_pp_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+    split_tensor_along_last_dim,
+    tensor_model_parallel_all_gather,
+)
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
+from vllm.model_executor.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+from vllm.model_executor.layers.pooler import DispatchPooler, Pooler
+from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.rotary_embedding import get_rope
-from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
-    ParallelLMHead, VocabParallelEmbedding)
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
-from vllm.model_executor.sampling_metadata import SamplingMetadata
-from vllm.sequence import SamplerOutput
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .interfaces_base import default_pooling_type
+from .utils import (
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
 
 
 class InternLM2MLP(nn.Module):
-
     def __init__(
         self,
         hidden_size: int,
         intermediate_size: int,
         hidden_act: str,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ) -> None:
         super().__init__()
         self.gate_up_proj = MergedColumnParallelLinear(
-            hidden_size, [intermediate_size] * 2,
+            hidden_size,
+            [intermediate_size] * 2,
             bias=False,
-            quant_config=quant_config)
-        self.w2 = RowParallelLinear(intermediate_size,
-                                    hidden_size,
-                                    bias=False,
-                                    quant_config=quant_config)
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.w2 = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.w2",
+        )
         if hidden_act != "silu":
-            raise ValueError(f"Unsupported activation: {hidden_act}. "
-                             "Only silu is supported for now.")
+            raise ValueError(
+                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
+            )
         self.act_fn = SiluAndMul()
 
     def forward(self, x):
@@ -55,38 +86,39 @@ class InternLM2MLP(nn.Module):
 
 
 class InternLM2Attention(nn.Module):
-
     def __init__(
         self,
         hidden_size: int,
         num_heads: int,
         num_kv_heads: int,
-        rope_theta: float = 10000,
-        rope_scaling: Optional[Dict[str, Any]] = None,
+        rope_parameters: dict[str, Any] | None = None,
         max_position_embeddings: int = 8192,
-        quant_config: Optional[QuantizationConfig] = None,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ) -> None:
         super().__init__()
         self.hidden_size = hidden_size
-        tp_size = get_tensor_model_parallel_world_size()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.tp_rank = get_tensor_model_parallel_rank()
         self.total_num_heads = num_heads
-        assert self.total_num_heads % tp_size == 0
-        self.num_heads = self.total_num_heads // tp_size
+        assert self.total_num_heads % self.tp_size == 0
+        self.num_heads = self.total_num_heads // self.tp_size
         self.total_num_kv_heads = num_kv_heads
-        if self.total_num_kv_heads >= tp_size:
+        if self.total_num_kv_heads >= self.tp_size:
             # Number of KV heads is greater than TP size, so we partition
             # the KV heads across multiple tensor parallel GPUs.
-            assert self.total_num_kv_heads % tp_size == 0
+            assert self.total_num_kv_heads % self.tp_size == 0
         else:
             # Number of KV heads is less than TP size, so we replicate
             # the KV heads across multiple tensor parallel GPUs.
-            assert tp_size % self.total_num_kv_heads == 0
-        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+            assert self.tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // self.tp_size)
         self.head_dim = 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.key_value_groups = int(self.num_heads / self.num_kv_heads)
         self.scaling = self.head_dim**-0.5
-        self.rope_theta = rope_theta
         self.max_position_embeddings = max_position_embeddings
 
         self.wqkv = QKVParallelLinear(
@@ -96,93 +128,114 @@ class InternLM2Attention(nn.Module):
             self.total_num_kv_heads,
             bias=False,
             quant_config=quant_config,
+            prefix=f"{prefix}.wqkv",
         )
         self.wo = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             hidden_size,
             bias=False,
             quant_config=quant_config,
+            prefix=f"{prefix}.wo",
         )
 
         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,
+            rope_parameters=rope_parameters,
         )
-        self.attn = Attention(self.num_heads,
-                              self.head_dim,
-                              self.scaling,
-                              num_kv_heads=self.num_kv_heads)
+        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,
+            prefix=f"{prefix}.attn",
+        )
+
+    def split_qkv(self, qkv: torch.Tensor):
+        seq_len = qkv.shape[0]
+        if self.tp_size > 1:
+            qkv_map = [self.q_size, self.kv_size, self.kv_size] * self.tp_size
+            qkv = tensor_model_parallel_all_gather(qkv)
+            qkv = torch.split(qkv, qkv_map, dim=-1)
+            qkv = qkv[::3] + qkv[1::3] + qkv[2::3]
+            qkv = torch.cat(qkv, dim=-1)
+
+        qkv = qkv.view(
+            seq_len, self.total_num_kv_heads, self.key_value_groups + 2, self.head_dim
+        )
+        q, k, v = torch.split(qkv, [self.key_value_groups, 1, 1], dim=-2)
+        q = q.reshape(seq_len, self.q_size * self.tp_size)
+        k = k.reshape(seq_len, self.kv_size * self.tp_size)
+        v = v.reshape(seq_len, self.kv_size * self.tp_size)
+
+        if self.tp_size > 1:
+            splitter = partial(split_tensor_along_last_dim, num_partitions=self.tp_size)
+            q = splitter(q)[self.tp_rank]
+            k = splitter(k)[self.tp_rank]
+            v = splitter(v)[self.tp_rank]
+        return q, k, v
 
     def forward(
         self,
         positions: torch.Tensor,
         hidden_states: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
         qkv, _ = self.wqkv(hidden_states)
-        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k, v = self.split_qkv(qkv)
         q, k = self.rotary_emb(positions, q, k)
-        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
+        attn_output = self.attn(q, k, v)
         output, _ = self.wo(attn_output)
         return output
 
 
 class InternLMDecoderLayer(nn.Module):
-
     def __init__(
         self,
         config: PretrainedConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
-        rope_theta = getattr(config, "rope_theta", 10000)
-        rope_scaling = getattr(config, "rope_scaling", None)
-        max_position_embeddings = getattr(config, "max_position_embeddings",
-                                          8192)
+        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
         self.attention = InternLM2Attention(
             hidden_size=self.hidden_size,
             num_heads=config.num_attention_heads,
             num_kv_heads=config.num_key_value_heads,
-            rope_theta=rope_theta,
-            rope_scaling=rope_scaling,
+            rope_parameters=config.rope_parameters,
             max_position_embeddings=max_position_embeddings,
+            cache_config=cache_config,
             quant_config=quant_config,
+            prefix=f"{prefix}.attention",
         )
         self.feed_forward = InternLM2MLP(
             hidden_size=self.hidden_size,
             intermediate_size=config.intermediate_size,
             hidden_act=config.hidden_act,
             quant_config=quant_config,
+            prefix=f"{prefix}.feed_forward",
         )
-        self.attention_norm = RMSNorm(config.hidden_size,
-                                      eps=config.rms_norm_eps)
+        self.attention_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.ffn_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
     def forward(
         self,
         positions: torch.Tensor,
         hidden_states: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: AttentionMetadata,
-        residual: Optional[torch.Tensor],
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        residual: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
         # Self Attention
         if residual is None:
             residual = hidden_states
             hidden_states = self.attention_norm(hidden_states)
         else:
-            hidden_states, residual = self.attention_norm(
-                hidden_states, residual)
+            hidden_states, residual = self.attention_norm(hidden_states, residual)
         hidden_states = self.attention(
             positions=positions,
             hidden_states=hidden_states,
-            kv_cache=kv_cache,
-            attn_metadata=attn_metadata,
         )
 
         # Fully Connected
@@ -191,106 +244,147 @@ class InternLMDecoderLayer(nn.Module):
         return hidden_states, residual
 
 
+@support_torch_compile
 class InternLM2Model(nn.Module):
-
     def __init__(
         self,
-        config: PretrainedConfig,
-        quant_config: Optional[QuantizationConfig] = None,
-    ) -> None:
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        layer_type: type[InternLMDecoderLayer] = InternLMDecoderLayer,
+    ):
         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.tok_embeddings = VocabParallelEmbedding(
             config.vocab_size,
             config.hidden_size,
         )
-        self.layers = nn.ModuleList([
-            InternLMDecoderLayer(config, quant_config)
-            for _ in range(config.num_hidden_layers)
-        ])
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: layer_type(
+                config, cache_config, quant_config, prefix=prefix
+            ),
+            prefix=f"{prefix}.layers",
+        )
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states", "residual"], config.hidden_size
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.tok_embeddings(input_ids)
 
     def forward(
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
-        kv_caches: List[torch.Tensor],
-        attn_metadata: AttentionMetadata,
-    ) -> torch.Tensor:
-        hidden_states = self.tok_embeddings(input_ids)
-        residual = None
-        for i in range(len(self.layers)):
-            layer = self.layers[i]
-            hidden_states, residual = layer(
-                positions,
-                hidden_states,
-                kv_caches[i],
-                attn_metadata,
-                residual,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.embed_input_ids(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for layer in islice(self.layers, self.start_layer, self.end_layer):
+            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)
         return hidden_states
 
 
-class InternLM2ForCausalLM(nn.Module):
+class InternLM2ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
+    packed_modules_mapping = {
+        "wqkv": ["wqkv"],
+        "gate_up_proj": ["w1", "w3"],
+    }
 
     def __init__(
         self,
-        config: PretrainedConfig,
-        quant_config: Optional[QuantizationConfig] = None,
-    ) -> None:
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        model_type: type[InternLM2Model] = InternLM2Model,
+    ):
         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 = InternLM2Model(config, quant_config)
-        self.output = ParallelLMHead(config.vocab_size, config.hidden_size)
+
+        self.model = model_type(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
+        )
+        self.output = ParallelLMHead(
+            config.vocab_size,
+            config.hidden_size,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "output"),
+        )
+        if self.config.tie_word_embeddings:
+            self.output.weight = self.model.tok_embeddings.weight
         self.logits_processor = LogitsProcessor(config.vocab_size)
-        self.sampler = Sampler()
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.embed_input_ids(input_ids)
 
     def forward(
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
-        kv_caches: List[torch.Tensor],
-        attn_metadata: AttentionMetadata,
+        intermediate_tensors: IntermediateTensors | None,
+        inputs_embeds: torch.Tensor | None = None,
     ) -> torch.Tensor:
-        hidden_states = self.model(input_ids, positions, kv_caches,
-                                   attn_metadata)
+        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.output.weight, hidden_states,
-                                       sampling_metadata)
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        logits = self.logits_processor(self.output, hidden_states)
         return logits
 
-    def sample(
-        self,
-        logits: 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]]):
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         stacked_params_mapping = [
             # (param_name, shard_name, shard_id)
             ("gate_up_proj", "w1", 0),
             ("gate_up_proj", "w3", 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
-            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+            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)
@@ -299,25 +393,61 @@ class InternLM2ForCausalLM(nn.Module):
                 # 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]
-                if "wqkv" in name:
-                    config = self.config
-                    kv_groups = (config.num_attention_heads //
-                                 config.num_key_value_heads)
-                    head_dim = config.hidden_size // config.num_attention_heads
-                    loaded_weight = loaded_weight.view(-1, 2 + kv_groups,
-                                                       head_dim,
-                                                       loaded_weight.shape[-1])
-                    wq, wk, wv = torch.split(loaded_weight, [kv_groups, 1, 1],
-                                             dim=1)
-                    wq = wq.reshape(-1, wq.shape[-1])
-                    wk = wk.reshape(-1, wk.shape[-1])
-                    wv = wv.reshape(-1, wv.shape[-1])
-                    weight_loader = param.weight_loader
-                    weight_loader(param, wq, 'q')
-                    weight_loader(param, wk, 'k')
-                    weight_loader(param, wv, 'v')
-                else:
-                    weight_loader = getattr(param, "weight_loader",
-                                            default_weight_loader)
-                    weight_loader(param, loaded_weight)
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+@default_pooling_type("ALL")
+class InternLM2ForRewardModel(InternLM2ForCausalLM):
+    is_pooling_model = True
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        model_type: type[InternLM2Model] = InternLM2Model,
+    ):
+        super().__init__(vllm_config=vllm_config, prefix=prefix, model_type=model_type)
+
+        for attr in ("output", "logits_processor"):
+            delattr(self, attr)
+
+        config = vllm_config.model_config.hf_config
+        self.head_dtype = vllm_config.model_config.head_dtype
+
+        self.v_head = RowParallelLinear(
+            config.hidden_size,
+            1,
+            bias=False,
+            input_is_parallel=False,
+            params_dtype=self.head_dtype,
+            prefix=maybe_prefix(prefix, "v_head"),
+            return_bias=False,
+        )
+
+        pooler_config = vllm_config.model_config.pooler_config
+        assert pooler_config is not None
+
+        self.pooler = DispatchPooler(
+            {"token_classify": Pooler.for_token_classify(pooler_config)}
+        )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        hidden_states = self.model(
+            input_ids, positions, intermediate_tensors, inputs_embeds
+        )
+        hidden_states = hidden_states.to(self.head_dtype)
+        logits = self.v_head(hidden_states)
+        return logits