Sync from v0.13

vllm/model_executor/models/gpt_j.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/gptj/modeling_gptj.py
 # Copyright 2023 The vLLM team.
@@ -16,36 +18,54 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only GPT-J 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 GPTJConfig
 
-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)
-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
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+)
+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 GPTJAttention(nn.Module):
-
     def __init__(
         self,
         config: GPTJConfig,
-        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
@@ -58,12 +78,14 @@ class GPTJAttention(nn.Module):
             self.total_num_heads,
             bias=False,
             quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
         )
         self.out_proj = RowParallelLinear(
             config.hidden_size,
             config.hidden_size,
             bias=False,
             quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
         )
 
         tp_world_size = get_tensor_model_parallel_world_size()
@@ -73,40 +95,44 @@ class GPTJAttention(nn.Module):
         scaling = self.head_size**-0.5
         assert getattr(config, "rotary", True)
         assert config.rotary_dim % 2 == 0
-        rope_theta = getattr(config, "rope_theta", 10000)
-        max_position_embeddings = getattr(config, "max_position_embeddings",
-                                          8192)
+        rope_parameters = getattr(config, "rope_parameters", {})
+        rope_parameters["partial_rotary_factor"] = config.rotary_dim / self.head_size
+        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
         self.rotary_emb = get_rope(
             self.head_size,
-            rotary_dim=config.rotary_dim,
             max_position=max_position_embeddings,
-            base=rope_theta,
+            rope_parameters=rope_parameters,
             is_neox_style=False,
         )
-        self.attn = Attention(self.num_heads, self.head_size, scaling)
+        self.attn = Attention(
+            self.num_heads,
+            self.head_size,
+            scaling,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
 
     def forward(
         self,
         position_ids: torch.Tensor,
         hidden_states: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
         qkv, _ = self.qkv_proj(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)
         attn_output, _ = self.out_proj(attn_output)
         return attn_output
 
 
 class GPTJMLP(nn.Module):
-
     def __init__(
         self,
         intermediate_size: int,
         config: GPTJConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ):
         super().__init__()
         hidden_size = config.n_embd
@@ -114,14 +140,15 @@ class GPTJMLP(nn.Module):
             hidden_size,
             intermediate_size,
             quant_config=quant_config,
+            prefix=f"{prefix}.fc_in",
         )
         self.fc_out = RowParallelLinear(
             intermediate_size,
             hidden_size,
             quant_config=quant_config,
+            prefix=f"{prefix}.fc_out",
         )
-        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.fc_in(hidden_states)
@@ -131,123 +158,88 @@ class GPTJMLP(nn.Module):
 
 
 class GPTJBlock(nn.Module):
-
     def __init__(
         self,
         config: GPTJConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ):
         super().__init__()
-        inner_dim = (4 * config.n_embd
-                     if config.n_inner is None else config.n_inner)
+        inner_dim = 4 * config.n_embd if config.n_inner is None else config.n_inner
         self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
-        self.attn = GPTJAttention(config, quant_config)
-        self.mlp = GPTJMLP(inner_dim, config, quant_config)
+        self.attn = GPTJAttention(
+            config, cache_config, quant_config, prefix=f"{prefix}.attn"
+        )
+        self.mlp = GPTJMLP(inner_dim, config, quant_config, prefix=f"{prefix}.mlp")
 
     def forward(
         self,
         position_ids: torch.Tensor,
         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(
             position_ids=position_ids,
             hidden_states=hidden_states,
-            kv_cache=kv_cache,
-            attn_metadata=attn_metadata,
         )
         mlp_output = self.mlp(hidden_states)
         hidden_states = attn_output + mlp_output + residual
         return hidden_states
 
 
+@support_torch_compile
 class GPTJModel(nn.Module):
-
-    def __init__(
-        self,
-        config: GPTJConfig,
-        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.quant_config = quant_config
         self.embed_dim = config.n_embd
         self.wte = VocabParallelEmbedding(
             config.vocab_size,
             self.embed_dim,
         )
-        self.h = nn.ModuleList(
-            [GPTJBlock(config, quant_config) for _ in range(config.n_layer)])
+        self.start_layer, self.end_layer, self.h = make_layers(
+            config.n_layer,
+            lambda prefix: GPTJBlock(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:
-        hidden_states = self.wte(input_ids)
-        for i in range(len(self.h)):
-            layer = self.h[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.h, 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.ln_f(hidden_states)
         return hidden_states
 
-
-class GPTJForCausalLM(nn.Module):
-
-    def __init__(
-        self,
-        config: GPTJConfig,
-        quant_config: Optional[QuantizationConfig] = None,
-    ):
-        super().__init__()
-        self.config = config
-        self.quant_config = quant_config
-        assert not config.tie_word_embeddings
-        self.transformer = GPTJModel(config, quant_config)
-        self.lm_head = ParallelLMHead(
-            config.vocab_size,
-            config.n_embd,
-            bias=True,
-        )
-        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, self.lm_head.bias)
-        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)
             ("qkv_proj", "q_proj", "q"),
@@ -257,25 +249,98 @@ class GPTJForCausalLM(nn.Module):
             ("gate_up_proj", "up_proj", 1),
         ]
         params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
         for name, loaded_weight in weights:
             if "attn.bias" in name or "attn.masked_bias" in name:
                 continue
-            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+
+            if self.quant_config is not None and (
+                scale_name := self.quant_config.get_cache_scale(name)
+            ):
+                # Loading kv cache quantization scales
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                loaded_weight = (
+                    loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
+                )
+                weight_loader(param, loaded_weight)
+                loaded_params.add(scale_name)
+                continue
+
+            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)
                 break
             else:
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
                 # 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 = 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 GPTJForCausalLM(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
+        assert not config.tie_word_embeddings
+        self.transformer = GPTJModel(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "transformer")
+        )
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.n_embd,
+            bias=True,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"),
+        )
+        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, self.lm_head.bias)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)