Sync from v0.13

vllm/model_executor/models/gpt_neox.py

@@ -1,4 +1,6 @@
-# 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/gpt_neox/modeling_gpt_neox.py
 # Copyright 2023 The vLLM team.
@@ -16,36 +18,51 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only GPT-NeoX 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 GPTNeoXConfig
 
-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_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.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 SupportsPP
+from .utils import (
+    AutoWeightsLoader,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
 
 
 class GPTNeoXAttention(nn.Module):
-
     def __init__(
         self,
         config: GPTNeoXConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ):
         super().__init__()
         self.total_num_heads = config.num_attention_heads
@@ -53,11 +70,9 @@ class GPTNeoXAttention(nn.Module):
         self.head_size = self.hidden_size // self.total_num_heads
         self.bias = getattr(config, "attention_bias", True)
 
-        tensor_model_parallel_world_size = (
-            get_tensor_model_parallel_world_size())
+        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
         assert self.total_num_heads % tensor_model_parallel_world_size == 0
-        self.num_heads = (self.total_num_heads //
-                          tensor_model_parallel_world_size)
+        self.num_heads = self.total_num_heads // tensor_model_parallel_world_size
 
         self.query_key_value = QKVParallelLinear(
             config.hidden_size,
@@ -65,62 +80,65 @@ class GPTNeoXAttention(nn.Module):
             self.total_num_heads,
             bias=self.bias,
             quant_config=quant_config,
+            prefix=f"{prefix}.query_key_value",
         )
         self.dense = RowParallelLinear(
             config.hidden_size,
             config.hidden_size,
             bias=self.bias,
             quant_config=quant_config,
+            prefix=f"{prefix}.dense",
         )
-        scaling = self.head_size**-0.5
-        rotary_dim = int(self.head_size * config.rotary_pct)
-        assert rotary_dim % 2 == 0
-        rope_theta = getattr(config, "rope_theta", 10000)
-        max_position_embeddings = getattr(config, "max_position_embeddings",
-                                          8192)
+        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
         self.rotary_emb = get_rope(
             self.head_size,
-            rotary_dim=rotary_dim,
             max_position=max_position_embeddings,
-            base=rope_theta,
+            rope_parameters=config.rope_parameters,
+        )
+        scaling = self.head_size**-0.5
+        self.attn = Attention(
+            self.num_heads,
+            self.head_size,
+            scaling,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
         )
-        self.attn = Attention(self.num_heads, self.head_size, scaling)
 
     def forward(
         self,
         position_ids: torch.Tensor,
         hidden_states: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
         qkv, _ = self.query_key_value(hidden_states)
         q, k, v = qkv.chunk(chunks=3, dim=-1)
         q, k = self.rotary_emb(position_ids, q, k)
-        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
+        attn_output = self.attn(q, k, v)
         output, _ = self.dense(attn_output)
         return output
 
 
 class GPTNeoXMLP(nn.Module):
-
     def __init__(
         self,
         config: GPTNeoXConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ):
         super().__init__()
         self.dense_h_to_4h = ColumnParallelLinear(
             config.hidden_size,
             config.intermediate_size,
             quant_config=quant_config,
+            prefix=f"{prefix}.dense_h_to_4h",
         )
         self.dense_4h_to_h = RowParallelLinear(
             config.intermediate_size,
             config.hidden_size,
             quant_config=quant_config,
+            prefix=f"{prefix}.dense_4h_to_h",
         )
-        self.act = get_act_fn(config.hidden_act, quant_config,
-                              config.intermediate_size)
+        self.act = get_act_fn(config.hidden_act)
 
     def forward(self, hidden_states):
         hidden_states, _ = self.dense_h_to_4h(hidden_states)
@@ -130,34 +148,35 @@ class GPTNeoXMLP(nn.Module):
 
 
 class GPTNeoXLayer(nn.Module):
-
     def __init__(
         self,
         config: GPTNeoXConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ):
         super().__init__()
         self.use_parallel_residual = config.use_parallel_residual
-        self.input_layernorm = nn.LayerNorm(config.hidden_size,
-                                            eps=config.layer_norm_eps)
-        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
-                                                     eps=config.layer_norm_eps)
-        self.attention = GPTNeoXAttention(config, quant_config)
+        self.input_layernorm = nn.LayerNorm(
+            config.hidden_size, eps=config.layer_norm_eps
+        )
+        self.post_attention_layernorm = nn.LayerNorm(
+            config.hidden_size, eps=config.layer_norm_eps
+        )
+        self.attention = GPTNeoXAttention(
+            config, cache_config, quant_config, prefix=f"{prefix}.attention"
+        )
         self.mlp = GPTNeoXMLP(config, quant_config)
 
     def forward(
         self,
         position_ids: torch.Tensor,
         hidden_states: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
         attn_input = self.input_layernorm(hidden_states)
         attn_output = self.attention(
             position_ids=position_ids,
             hidden_states=attn_input,
-            kv_cache=kv_cache,
-            attn_metadata=attn_metadata,
         )
 
         if self.use_parallel_residual:
@@ -177,101 +196,75 @@ class GPTNeoXLayer(nn.Module):
         return hidden_states
 
 
+@support_torch_compile
 class GPTNeoXModel(nn.Module):
-
-    def __init__(
-        self,
-        config: GPTNeoXConfig,
-        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
 
         self.embed_in = VocabParallelEmbedding(
             config.vocab_size,
             config.hidden_size,
         )
-        self.layers = nn.ModuleList([
-            GPTNeoXLayer(config, quant_config)
-            for _ in range(config.num_hidden_layers)
-        ])
-        self.final_layer_norm = nn.LayerNorm(config.hidden_size,
-                                             eps=config.layer_norm_eps)
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: GPTNeoXLayer(
+                config, cache_config, quant_config, prefix=prefix
+            ),
+            prefix=f"{prefix}.layers",
+        )
+        self.final_layer_norm = nn.LayerNorm(
+            config.hidden_size, eps=config.layer_norm_eps
+        )
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states"], config.hidden_size
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_in(input_ids)
 
     def forward(
         self,
         input_ids: torch.Tensor,
         position_ids: torch.Tensor,
-        kv_caches: List[torch.Tensor],
-        attn_metadata: AttentionMetadata,
-    ) -> torch.Tensor:
-        hidden_states = self.embed_in(input_ids)
-        for i in range(len(self.layers)):
-            layer = self.layers[i]
-            hidden_states = layer(
-                position_ids,
-                hidden_states,
-                kv_caches[i],
-                attn_metadata,
-            )
+        intermediate_tensors: IntermediateTensors | 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)
+        else:
+            hidden_states = intermediate_tensors["hidden_states"]
+        for layer in islice(self.layers, self.start_layer, self.end_layer):
+            hidden_states = layer(position_ids, hidden_states)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
         hidden_states = self.final_layer_norm(hidden_states)
         return hidden_states
 
-
-class GPTNeoXForCausalLM(nn.Module):
-
-    def __init__(
-        self,
-        config,
-        quant_config: Optional[QuantizationConfig] = None,
-    ):
-        super().__init__()
-        self.config = config
-        self.quant_config = quant_config
-        self.gpt_neox = GPTNeoXModel(config, quant_config)
-        self.embed_out = ParallelLMHead(
-            config.vocab_size,
-            config.hidden_size,
-        )
-        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.gpt_neox(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.embed_out.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())
+        loaded_params: set[str] = set()
         for name, loaded_weight in weights:
-            if ("attention.bias" in name or "attention.masked_bias" in name
-                    or "rotary_emb.inv_freq" in name):
+            if (
+                "attention.bias" in name
+                or "attention.masked_bias" in name
+                or "rotary_emb.inv_freq" in name
+            ):
                 continue
-            if ("rotary_emb.cos_cached" in name
-                    or "rotary_emb.sin_cached" in name):
+            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
                 # Models trained using OpenRLHF 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:
@@ -284,12 +277,64 @@ class GPTNeoXForCausalLM(nn.Module):
                 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_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 = getattr(param, "weight_loader", default_weight_loader)
             weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class GPTNeoXForCausalLM(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.gpt_neox = GPTNeoXModel(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "gpt_neox")
+        )
+        self.embed_out = ParallelLMHead(
+            config.vocab_size,
+            config.hidden_size,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "embed_out"),
+        )
+        if self.config.tie_word_embeddings:
+            self.embed_out.weight = self.gpt_neox.embed_in.weight
+        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.gpt_neox.make_empty_intermediate_tensors
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.gpt_neox.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.gpt_neox(
+            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.embed_out, hidden_states)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)