Sync from v0.13

vllm/model_executor/models/gpt2.py

@@ -1,9 +1,10 @@
-# coding=utf-8
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
 # Adapted from
 # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt2/modeling_gpt2.py
 # Copyright 2023 The vLLM team.
 # Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
-# Copyright (c) 2024 - 2024 Moore Threads Technology Co., Ltd("Moore Threads"). All rights reserved.
 # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -18,41 +19,59 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only GPT-2 model compatible with HuggingFace weights."""
-from typing import Iterable, List, Optional, Tuple
+
+from collections.abc import Iterable
+from itertools import islice
 
 import torch
 from torch import nn
 from transformers import GPT2Config
 
-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.parallel_state 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.linear import (ColumnParallelLinear,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    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.sampler import Sampler
+from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.vocab_parallel_embedding import (
-    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 ..layers.pooler import DispatchPooler, Pooler
+from .interfaces import SupportsCrossEncoding, SupportsPP
+from .utils import (
+    AutoWeightsLoader,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
 
 
 class GPT2Attention(nn.Module):
-
     def __init__(
         self,
         config: GPT2Config,
-        quant_config: Optional[QuantizationConfig] = None,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ):
         super().__init__()
         self.hidden_size = config.hidden_size
         total_num_heads = config.num_attention_heads
-        tensor_model_parallel_world_size = (
-            get_tensor_model_parallel_world_size())
+        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
         assert total_num_heads % tensor_model_parallel_world_size == 0
         self.num_heads = total_num_heads // tensor_model_parallel_world_size
         self.head_dim = self.hidden_size // total_num_heads
@@ -64,35 +83,42 @@ class GPT2Attention(nn.Module):
             total_num_heads,
             bias=True,
             quant_config=quant_config,
+            prefix=f"{prefix}.c_attn",
         )
         self.c_proj = RowParallelLinear(
             self.hidden_size,
             self.hidden_size,
             bias=True,
             quant_config=quant_config,
+            prefix=f"{prefix}.c_proj",
+        )
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            scale=self.scale,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
         )
-        self.attn = Attention(self.num_heads, self.head_dim, scale=self.scale)
 
     def forward(
         self,
         hidden_states: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
         qkv, _ = self.c_attn(hidden_states)
         q, k, v = qkv.chunk(chunks=3, dim=-1)
-        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
+        attn_output = self.attn(q, k, v)
         attn_output, _ = self.c_proj(attn_output)
         return attn_output
 
 
 class GPT2MLP(nn.Module):
-
     def __init__(
         self,
         intermediate_size: int,
         config: GPT2Config,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ):
         super().__init__()
         hidden_size = config.hidden_size
@@ -101,15 +127,16 @@ class GPT2MLP(nn.Module):
             intermediate_size,
             bias=True,
             quant_config=quant_config,
+            prefix=f"{prefix}.c_fc",
         )
         self.c_proj = RowParallelLinear(
             intermediate_size,
             hidden_size,
             bias=True,
             quant_config=quant_config,
+            prefix=f"{prefix}.c_proj",
         )
-        self.act = get_act_fn(config.activation_function, quant_config,
-                              intermediate_size)
+        self.act = get_act_fn(config.activation_function)
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         hidden_states, _ = self.c_fc(hidden_states)
@@ -119,35 +146,31 @@ class GPT2MLP(nn.Module):
 
 
 class GPT2Block(nn.Module):
-
     def __init__(
         self,
         config: GPT2Config,
-        quant_config: Optional[QuantizationConfig] = None,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ):
         super().__init__()
         hidden_size = config.hidden_size
-        inner_dim = (config.n_inner if config.n_inner is not None else 4 *
-                     hidden_size)
+        inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
 
         self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-        self.attn = GPT2Attention(config, quant_config)
+        self.attn = GPT2Attention(
+            config, cache_config, quant_config, prefix=f"{prefix}.attn"
+        )
         self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-        self.mlp = GPT2MLP(inner_dim, config, quant_config)
+        self.mlp = GPT2MLP(inner_dim, config, quant_config, prefix=f"{prefix}.mlp")
 
     def forward(
         self,
         hidden_states: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
         residual = hidden_states
         hidden_states = self.ln_1(hidden_states)
-        attn_output = self.attn(
-            hidden_states=hidden_states,
-            kv_cache=kv_cache,
-            attn_metadata=attn_metadata,
-        )
+        attn_output = self.attn(hidden_states=hidden_states)
         # residual connection
         hidden_states = attn_output + residual
 
@@ -159,99 +182,77 @@ class GPT2Block(nn.Module):
         return hidden_states
 
 
+@support_torch_compile
 class GPT2Model(nn.Module):
-
-    def __init__(
-        self,
-        config: GPT2Config,
-        quant_config: Optional[QuantizationConfig] = None,
-    ):
+    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
         assert not config.add_cross_attention
         assert not config.scale_attn_by_inverse_layer_idx
         assert not config.reorder_and_upcast_attn
         self.embed_dim = config.hidden_size
-        self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim)
+        self.wte = VocabParallelEmbedding(
+            config.vocab_size,
+            self.embed_dim,
+            quant_config=quant_config,
+            prefix=f"{prefix}.wte",
+        )
         self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
-        self.h = nn.ModuleList([
-            GPT2Block(config, quant_config)
-            for _ in range(config.num_hidden_layers)
-        ])
+        self.start_layer, self.end_layer, self.h = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: GPT2Block(config, cache_config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.h",
+        )
         self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states"], config.n_embd
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.wte(input_ids)
 
     def forward(
         self,
         input_ids: torch.Tensor,
         position_ids: torch.Tensor,
-        kv_caches: List[torch.Tensor],
-        attn_metadata: AttentionMetadata,
-    ) -> torch.Tensor:
-        inputs_embeds = self.wte(input_ids)
-        position_embeds = self.wpe(position_ids)
-        hidden_states = inputs_embeds + position_embeds
+        intermediate_tensors: IntermediateTensors | None,
+        inputs_embeds: torch.Tensor | None,
+    ) -> torch.Tensor | IntermediateTensors:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is None:
+                inputs_embeds = self.embed_input_ids(input_ids)
+            position_embeds = self.wpe(position_ids)
+            hidden_states = inputs_embeds + position_embeds
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
 
-        for i in range(len(self.h)):
-            layer = self.h[i]
-            hidden_states = layer(hidden_states, kv_caches[i], attn_metadata)
+        for layer in islice(self.h, self.start_layer, self.end_layer):
+            hidden_states = layer(hidden_states)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
 
         hidden_states = self.ln_f(hidden_states)
         return hidden_states
 
-
-class GPT2LMHeadModel(nn.Module):
-
-    def __init__(
-        self,
-        config: GPT2Config,
-        quant_config: Optional[QuantizationConfig] = None,
-    ):
-        super().__init__()
-        self.config = config
-        self.quant_config = quant_config
-        self.transformer = GPT2Model(config, quant_config)
-        self.lm_head_weight = self.transformer.wte.weight
-        self.logits_processor = LogitsProcessor(config.vocab_size)
-        self.sampler = Sampler()
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        kv_caches: List[torch.Tensor],
-        attn_metadata: AttentionMetadata,
-    ) -> torch.Tensor:
-        hidden_states = self.transformer(input_ids, positions, kv_caches,
-                                         attn_metadata)
-        return hidden_states
-
-    def compute_logits(self, hidden_states: torch.Tensor,
-                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
-        logits = self.logits_processor(self.lm_head_weight, hidden_states,
-                                       sampling_metadata)
-        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]:
         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:
-                # GPT-2 ties the weights of the embedding layer and the final
-                # linear layer.
-                continue
             if ".attn.bias" in name or ".attn.masked_bias" in name:
                 # Skip attention mask.
                 # NOTE: "c_attn.bias" should not be skipped.
                 continue
-            if not name.startswith("transformer."):
-                name = "transformer." + name
+
+            if is_pp_missing_parameter(name, self):
+                continue
+
             param = params_dict[name]
             # The HF's GPT-2 implementation uses Conv1D instead of Linear.
             # Because of this, we need to transpose the weights.
@@ -262,6 +263,135 @@ class GPT2LMHeadModel(nn.Module):
                 if not name.endswith(".weight"):
                     continue
                 loaded_weight = loaded_weight.t()
-            weight_loader = getattr(param, "weight_loader",
-                                    default_weight_loader)
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
             weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class GPT2LMHeadModel(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.transformer = GPT2Model(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "transformer")
+        )
+        self.lm_head = ParallelLMHead(
+            self.config.vocab_size,
+            self.config.hidden_size,
+            quant_config=quant_config,
+            prefix=f"{prefix}.lm_head",
+        )
+        if self.config.tie_word_embeddings:
+            self.lm_head = self.lm_head.tie_weights(self.transformer.wte)
+
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.transformer.make_empty_intermediate_tensors
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.transformer.embed_input_ids(input_ids)
+
+    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.transformer(
+            input_ids, positions, intermediate_tensors, inputs_embeds
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        logits = self.logits_processor(self.lm_head, hidden_states)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        weights = _add_transformer_prefix(weights)
+        return loader.load_weights(weights)
+
+
+class GPT2ForSequenceClassification(nn.Module, SupportsCrossEncoding):
+    """GPT2 Model for sequence classification.
+
+    This class expands GPT2Model with pooling and score functions - last token
+    is being used for classification.
+
+    Attributes:
+        transformer: An instance of GPT2Model used for forward operations.
+        score: A layer for calculating logits.
+        _pooler: An instance of Pooler used for pooling operations.
+    """
+
+    is_pooling_model = True
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        self.transformer = GPT2Model(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "gpt2")
+        )
+        self.score = nn.Linear(
+            config.n_embd,
+            config.num_labels,
+            bias=False,
+            dtype=vllm_config.model_config.head_dtype,
+        )
+
+        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, classifier=self.score
+                ),
+                "classify": Pooler.for_classify(
+                    pooler_config, classifier=self.score, act_fn="classify"
+                ),
+                "score": Pooler.for_classify(
+                    pooler_config, classifier=self.score, act_fn="score"
+                ),
+            }
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.transformer.embed_input_ids(input_ids)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        hidden_states = self.transformer(
+            input_ids=input_ids,
+            position_ids=positions,
+            inputs_embeds=inputs_embeds,
+            intermediate_tensors=intermediate_tensors,
+        )
+        return hidden_states
+
+
+def _add_transformer_prefix(
+    weights: Iterable[tuple[str, torch.Tensor]],
+) -> Iterable[tuple[str, torch.Tensor]]:
+    for name, tensor in weights:
+        if not name.startswith("transformer.") and not name.startswith("lm_head"):
+            name = "transformer." + name
+        yield name, tensor