Sync from v0.13

vllm/model_executor/models/mpt.py

@@ -1,35 +1,50 @@
-# coding=utf-8
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
 # Adapted from https://huggingface.co/mosaicml/mpt-7b/tree/main
 import math
-from typing import Iterable, List, Optional, Tuple
+from collections.abc import Iterable
+from itertools import islice
 
 import torch
 import torch.nn as nn
+from transformers import MptConfig
 
-from vllm.attention import Attention, AttentionMetadata
-from vllm.distributed import (get_tensor_model_parallel_rank,
-                              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,
+)
 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.vocab_parallel_embedding import (
-    VocabParallelEmbedding)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import 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.transformers_utils.configs.mpt import MPTConfig
+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,
+)
 
 
 def _get_alibi_slopes(
     total_num_heads: int,
     alibi_bias_max: int,
 ) -> torch.Tensor:
-    next_power_of_2 = 2**math.ceil(math.log2(total_num_heads))
+    next_power_of_2 = 2 ** math.ceil(math.log2(total_num_heads))
     m = torch.arange(1, next_power_of_2 + 1, dtype=torch.float32)
     m = m.mul(alibi_bias_max / next_power_of_2)
     slopes = 1.0 / torch.pow(2, m)
@@ -39,25 +54,26 @@ def _get_alibi_slopes(
 
 
 class MPTAttention(nn.Module):
-
     def __init__(
         self,
-        config: MPTConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        config: MptConfig,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ):
         super().__init__()
         self.d_model = config.d_model
         self.total_num_heads = config.n_heads
         self.head_dim = self.d_model // self.total_num_heads
-        self.clip_qkv = config.attn_config["clip_qkv"]
-        self.qk_ln = config.attn_config["qk_ln"]
-        self.alibi_bias_max = config.attn_config["alibi_bias_max"]
+        self.clip_qkv = config.attn_config.clip_qkv
+        self.qk_ln = config.attn_config.qk_ln
+        self.alibi_bias_max = config.attn_config.alibi_bias_max
         if "kv_n_heads" in config.attn_config:
-            self.total_num_kv_heads = config.attn_config['kv_n_heads']
+            self.total_num_kv_heads = config.attn_config.kv_n_heads
         else:
             self.total_num_kv_heads = self.total_num_heads
-        assert not config.attn_config["prefix_lm"]
-        assert config.attn_config["alibi"]
+        assert not config.attn_config.prefix_lm
+        assert config.attn_config.alibi
 
         # pylint: disable=invalid-name
         self.Wqkv = QKVParallelLinear(
@@ -67,6 +83,7 @@ class MPTAttention(nn.Module):
             self.total_num_kv_heads,
             bias=not config.no_bias,
             quant_config=quant_config,
+            prefix=f"{prefix}.Wqkv",
         )
         if self.qk_ln:
             self.q_ln = nn.LayerNorm(self.d_model)
@@ -76,6 +93,7 @@ class MPTAttention(nn.Module):
             self.d_model,
             bias=not config.no_bias,
             quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
         )
 
         tp_world_size = get_tensor_model_parallel_world_size()
@@ -97,24 +115,26 @@ class MPTAttention(nn.Module):
         tp_rank = get_tensor_model_parallel_rank()
         head_start = tp_rank * self.num_heads
         head_end = (tp_rank + 1) * self.num_heads
-        alibi_slopes = _get_alibi_slopes(self.total_num_heads,
-                                         self.alibi_bias_max)
+        alibi_slopes = _get_alibi_slopes(self.total_num_heads, self.alibi_bias_max)
         alibi_slopes = alibi_slopes[head_start:head_end].tolist()
 
         self.head_dim = self.d_model // self.total_num_heads
         scaling = self.head_dim**-0.5
-        self.attn = Attention(self.num_heads,
-                              self.head_dim,
-                              scaling,
-                              alibi_slopes=alibi_slopes,
-                              num_kv_heads=self.num_kv_heads)
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            scaling,
+            alibi_slopes=alibi_slopes,
+            num_kv_heads=self.num_kv_heads,
+            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:
         del position_ids  # unused.
         qkv, _ = self.Wqkv(hidden_states)
@@ -124,17 +144,17 @@ class MPTAttention(nn.Module):
         if self.qk_ln:
             q = self.q_ln(q)
             k = self.k_ln(k)
-        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
+        attn_output = self.attn(q, k, v)
         output, _ = self.out_proj(attn_output)
         return output
 
 
 class MPTMLP(nn.Module):
-
     def __init__(
         self,
-        config: MPTConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        config: MptConfig,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ):
         super().__init__()
         hidden_size = config.d_model
@@ -145,13 +165,15 @@ class MPTMLP(nn.Module):
             intermediate_size,
             bias=not config.no_bias,
             quant_config=quant_config,
+            prefix=f"{prefix}.up_proj",
         )
-        self.act = get_act_fn("gelu", quant_config, intermediate_size)
+        self.act = get_act_fn("gelu")
         self.down_proj = RowParallelLinear(
             intermediate_size,
             hidden_size,
             bias=not config.no_bias,
             quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
         )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -162,32 +184,31 @@ class MPTMLP(nn.Module):
 
 
 class MPTBlock(nn.Module):
-
     def __init__(
         self,
-        config: MPTConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        config: MptConfig,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ):
         super().__init__()
         hidden_size = config.d_model
         self.norm_1 = nn.LayerNorm(hidden_size)
-        self.attn = MPTAttention(config, quant_config)
+        self.attn = MPTAttention(
+            config, cache_config, quant_config, prefix=f"{prefix}.attn"
+        )
         self.norm_2 = nn.LayerNorm(hidden_size)
-        self.ffn = MPTMLP(config, quant_config)
+        self.ffn = MPTMLP(config, quant_config, prefix=f"{prefix}.ffn")
 
     def forward(
         self,
         position_ids: torch.Tensor,
         hidden_states: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
         x = self.norm_1(hidden_states)
         x = self.attn(
             position_ids=position_ids,
             hidden_states=x,
-            kv_cache=kv_cache,
-            attn_metadata=attn_metadata,
         )
         hidden_states = hidden_states + x
         x = self.norm_2(hidden_states)
@@ -196,14 +217,15 @@ class MPTBlock(nn.Module):
         return hidden_states
 
 
+@support_torch_compile
 class MPTModel(nn.Module):
-
-    def __init__(
-        self,
-        config: MPTConfig,
-        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
+
         assert config.embedding_fraction == 1.0
         assert config.norm_type == "low_precision_layernorm"
 
@@ -211,85 +233,103 @@ class MPTModel(nn.Module):
             config.vocab_size,
             config.d_model,
         )
-        self.blocks = nn.ModuleList(
-            [MPTBlock(config, quant_config) for _ in range(config.n_layers)])
+        self.start_layer, self.end_layer, self.blocks = make_layers(
+            config.n_layers,
+            lambda prefix: MPTBlock(config, cache_config, quant_config, prefix=prefix),
+            prefix=f"{prefix}.blocks",
+        )
         self.norm_f = nn.LayerNorm(config.d_model)
         if config.no_bias:
             for module in self.modules():
-                if hasattr(module, "bias") and isinstance(
-                        module.bias, nn.Parameter):
+                if hasattr(module, "bias") and isinstance(module.bias, nn.Parameter):
                     # Remove the bias term in Linear and LayerNorm.
                     module.register_parameter("bias", None)
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states"], config.d_model
+        )
+
+    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.blocks)):
-            block = self.blocks[i]
-            hidden_states = block(
-                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:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+
+        for block in islice(self.blocks, self.start_layer, self.end_layer):
+            hidden_states = block(position_ids, hidden_states)
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
         hidden_states = self.norm_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:
+            # 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(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
 
-class MPTForCausalLM(nn.Module):
 
-    def __init__(
-        self,
-        config: MPTConfig,
-        quant_config: Optional[QuantizationConfig] = None,
-    ):
+class MPTForCausalLM(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
         assert config.tie_word_embeddings
         self.quant_config = quant_config
 
-        self.transformer = MPTModel(config, quant_config)
-        self.lm_head_weight = self.transformer.wte.weight
+        self.transformer = MPTModel(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "transformer")
+        )
+        self.lm_head = self.transformer.wte
         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:
-            # Skip loading extra bias for GPTQ models.
-            if name.endswith(".bias") and name not in params_dict:
-                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]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)