Sync from v0.13

vllm/model_executor/models/gpt_bigcode.py

@@ -1,10 +1,11 @@
-# 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 2023 CTranslate2, and Michael Feil
 # 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");
@@ -19,44 +20,57 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only GPTBigCode 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 GPTBigCodeConfig
 
-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.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 .interfaces import SupportsLoRA, SupportsPP
+from .utils import (
+    AutoWeightsLoader,
+    is_pp_missing_parameter,
+    make_empty_intermediate_tensors_factory,
+    make_layers,
+    maybe_prefix,
+)
 
 
 class GPTBigCodeAttention(nn.Module):
-
     def __init__(
         self,
         config: GPTBigCodeConfig,
-        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
-        self.tensor_model_parallel_world_size = (
-            get_tensor_model_parallel_world_size())
+        self.tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
         assert total_num_heads % self.tensor_model_parallel_world_size == 0
-        self.num_heads = (total_num_heads //
-                          self.tensor_model_parallel_world_size)
+        self.num_heads = total_num_heads // self.tensor_model_parallel_world_size
         self.head_dim = self.hidden_size // total_num_heads
         self.scale = self.head_dim**-0.5
 
@@ -75,6 +89,7 @@ class GPTBigCodeAttention(nn.Module):
             total_num_kv_heads,
             bias=True,
             quant_config=quant_config,
+            prefix=f"{prefix}.c_attn",
         )
 
         self.c_proj = RowParallelLinear(
@@ -82,38 +97,43 @@ class GPTBigCodeAttention(nn.Module):
             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,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
         )
-        self.attn = Attention(self.num_heads,
-                              self.head_dim,
-                              scale=self.scale,
-                              num_kv_heads=self.num_kv_heads)
 
     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.split(
             [
                 self.hidden_size // self.tensor_model_parallel_world_size,
-                self.kv_dim, self.kv_dim
+                self.kv_dim,
+                self.kv_dim,
             ],
             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 GPTBigMLP(nn.Module):
-
     def __init__(
         self,
         intermediate_size: int,
         config: GPTBigCodeConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ):
         super().__init__()
         hidden_size = config.hidden_size
@@ -122,15 +142,16 @@ class GPTBigMLP(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)
@@ -140,34 +161,32 @@ class GPTBigMLP(nn.Module):
 
 
 class GPTBigCodeBlock(nn.Module):
-
     def __init__(
         self,
         config: GPTBigCodeConfig,
-        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 = GPTBigCodeAttention(config, quant_config)
+        self.attn = GPTBigCodeAttention(
+            config, cache_config, quant_config, prefix=f"{prefix}.attn"
+        )
         self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-        self.mlp = GPTBigMLP(inner_dim, config, quant_config)
+        self.mlp = GPTBigMLP(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,
         )
         # residual connection
         hidden_states = attn_output + residual
@@ -180,96 +199,141 @@ class GPTBigCodeBlock(nn.Module):
         return hidden_states
 
 
+@support_torch_compile
 class GPTBigCodeModel(nn.Module):
-
-    def __init__(
-        self,
-        config: GPTBigCodeConfig,
-        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
 
         self.embed_dim = config.hidden_size
 
-        self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim)
+        self.vocab_size = config.vocab_size
+        self.wte = VocabParallelEmbedding(
+            self.vocab_size, self.embed_dim, org_num_embeddings=config.vocab_size
+        )
         self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
-        self.h = nn.ModuleList([
-            GPTBigCodeBlock(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: GPTBigCodeBlock(
+                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 = None,
+    ) -> torch.Tensor | IntermediateTensors:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is None:
+                inputs_embeds = self.embed_input_ids(input_ids)
+            hidden_states = inputs_embeds + self.wpe(position_ids)
+        else:
+            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
 
+    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 ".attn.bias" in name:
+                # Skip attention mask.
+                # NOTE: "c_attn.bias" should not be skipped.
+                continue
+            if is_pp_missing_parameter(name, self):
+                continue
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            # TODO (@robertgshaw2-neuralmagic): move to fp8 linear method
+            if "c_attn.input_scale" in name:
+                weight_loader(param, loaded_weight, "q")
+                weight_loader(param, loaded_weight, "k")
+                weight_loader(param, loaded_weight, "v")
+            else:
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
 
-class GPTBigCodeForCausalLM(nn.Module):
 
-    def __init__(
-        self,
-        config: GPTBigCodeConfig,
-        quant_config: Optional[QuantizationConfig] = None,
-    ):
+class GPTBigCodeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    packed_modules_mapping = {"c_attn": ["c_attn"]}
+
+    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 = GPTBigCodeModel(config, quant_config)
-        self.lm_head_weight = self.transformer.wte.weight
+        self.transformer = GPTBigCodeModel(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "transformer")
+        )
+        if self.config.tie_word_embeddings:
+            self.lm_head = self.transformer.wte
+        else:
+            self.lm_head = ParallelLMHead(
+                self.transformer.vocab_size,
+                self.transformer.embed_dim,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+
         self.logits_processor = LogitsProcessor(config.vocab_size)
-        self.sampler = Sampler()
+        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,
-        kv_caches: List[torch.Tensor],
-        attn_metadata: AttentionMetadata,
-    ) -> torch.Tensor:
-        hidden_states = self.transformer(input_ids, positions, kv_caches,
-                                         attn_metadata)
+        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,
-                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
-        logits = self.logits_processor(self.lm_head_weight, hidden_states,
-                                       sampling_metadata)
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        logits = self.logits_processor(self.lm_head, 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]]):
-        params_dict = dict(self.named_parameters(remove_duplicate=False))
-        for name, loaded_weight in weights:
-            if "lm_head.weight" in name:
-                continue
-            if ".attn.bias" in name:
-                # Skip attention mask.
-                # NOTE: "c_attn.bias" should not be skipped.
-                continue
-            param = params_dict[name]
-            weight_loader = getattr(param, "weight_loader",
-                                    default_weight_loader)
-            weight_loader(param, loaded_weight)
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        skip_prefixes = None
+        if self.config.tie_word_embeddings:
+            skip_prefixes = ["lm_head."]
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=skip_prefixes,
+        )
+        return loader.load_weights(weights)