Sync from v0.13

vllm/model_executor/models/bloom.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/bloom/modeling_bloom.py
 # Copyright 2023 The vLLM team.
@@ -16,35 +18,52 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only BLOOM model compatible with HuggingFace weights."""
+
 import math
-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 BloomConfig
 
-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.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 SupportsPP, SupportsQuant
+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) -> torch.Tensor:
-    closest_power_of_2 = 2**math.floor(math.log2(total_num_heads))
+    closest_power_of_2 = 2 ** math.floor(math.log2(total_num_heads))
     base = torch.tensor(
-        2**(-(2**-(math.log2(closest_power_of_2) - 3))),
+        2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))),
         dtype=torch.float32,
     )
     powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
@@ -52,26 +71,26 @@ def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
 
     if closest_power_of_2 != total_num_heads:
         extra_base = torch.tensor(
-            2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))),
+            2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))),
             dtype=torch.float32,
         )
-        num_remaining_heads = min(closest_power_of_2,
-                                  total_num_heads - closest_power_of_2)
-        extra_powers = torch.arange(start=1,
-                                    end=1 + 2 * num_remaining_heads,
-                                    step=2,
-                                    dtype=torch.int32)
-        slopes = torch.cat(
-            [slopes, torch.pow(extra_base, extra_powers)], dim=0)
+        num_remaining_heads = min(
+            closest_power_of_2, total_num_heads - closest_power_of_2
+        )
+        extra_powers = torch.arange(
+            start=1, end=1 + 2 * num_remaining_heads, step=2, dtype=torch.int32
+        )
+        slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
     return slopes
 
 
 class BloomAttention(nn.Module):
-
     def __init__(
         self,
         config: BloomConfig,
-        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
@@ -89,12 +108,14 @@ class BloomAttention(nn.Module):
             self.total_num_heads,
             bias=True,
             quant_config=quant_config,
+            prefix=f"{prefix}.query_key_value",
         )
         self.dense = RowParallelLinear(
             self.hidden_size,
             self.hidden_size,
             bias=True,
             quant_config=quant_config,
+            prefix=f"{prefix}.dense",
         )
 
         # Create the alibi slopes and slice them.
@@ -105,32 +126,35 @@ class BloomAttention(nn.Module):
         alibi_slopes = alibi_slopes[head_start:head_end].tolist()
 
         scaling = self.head_dim**-0.5
-        self.attn = Attention(self.num_heads,
-                              self.head_dim,
-                              scaling,
-                              alibi_slopes=alibi_slopes)
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            scaling,
+            alibi_slopes=alibi_slopes,
+            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.query_key_value(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)
         output, _ = self.dense(attn_output)
         return output
 
 
 class BloomMLP(nn.Module):
-
     def __init__(
         self,
         config: BloomConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ):
         super().__init__()
         hidden_size = config.hidden_size
@@ -138,12 +162,14 @@ class BloomMLP(nn.Module):
             hidden_size,
             4 * hidden_size,
             quant_config=quant_config,
+            prefix=f"{prefix}.dense_h_to_4h",
         )
-        self.gelu_impl = get_act_fn("gelu", quant_config, 4 * hidden_size)
+        self.gelu_impl = get_act_fn("gelu")
         self.dense_4h_to_h = RowParallelLinear(
             4 * hidden_size,
             hidden_size,
             quant_config=quant_config,
+            prefix=f"{prefix}.dense_4h_to_h",
         )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -154,30 +180,32 @@ class BloomMLP(nn.Module):
 
 
 class BloomBlock(nn.Module):
-
     def __init__(
         self,
         config: BloomConfig,
-        quant_config: Optional[QuantizationConfig] = None,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
     ):
         super().__init__()
         hidden_size = config.hidden_size
 
-        self.input_layernorm = nn.LayerNorm(hidden_size,
-                                            eps=config.layer_norm_epsilon)
-        self.self_attention = BloomAttention(config, quant_config)
+        self.input_layernorm = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.self_attention = BloomAttention(
+            config, cache_config, quant_config, prefix=f"{prefix}.self_attention"
+        )
         self.post_attention_layernorm = nn.LayerNorm(
-            hidden_size, eps=config.layer_norm_epsilon)
-        self.mlp = BloomMLP(config, quant_config)
+            hidden_size, eps=config.layer_norm_epsilon
+        )
+        self.mlp = BloomMLP(config, quant_config, prefix=f"{prefix}.mlp")
         self.apply_residual_connection_post_layernorm = (
-            config.apply_residual_connection_post_layernorm)
+            config.apply_residual_connection_post_layernorm
+        )
 
     def forward(
         self,
         position_ids: torch.Tensor,
         hidden_states: torch.Tensor,
-        kv_cache: torch.Tensor,
-        attn_metadata: AttentionMetadata,
     ) -> torch.Tensor:
         # Layer norm at the beginning of the transformer layer.
         layernorm_output = self.input_layernorm(hidden_states)
@@ -192,8 +220,6 @@ class BloomBlock(nn.Module):
         attention_output = self.self_attention(
             position_ids=position_ids,
             hidden_states=layernorm_output,
-            kv_cache=kv_cache,
-            attn_metadata=attn_metadata,
         )
         attention_output = attention_output + residual
         layernorm_output = self.post_attention_layernorm(attention_output)
@@ -209,14 +235,16 @@ class BloomBlock(nn.Module):
         return output
 
 
+@support_torch_compile
 class BloomModel(nn.Module):
-
-    def __init__(
-        self,
-        config: BloomConfig,
-        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_dim = config.hidden_size
 
         # Embedding + LN Embedding
@@ -225,85 +253,56 @@ class BloomModel(nn.Module):
             self.embed_dim,
         )
         self.word_embeddings_layernorm = nn.LayerNorm(
-            self.embed_dim, eps=config.layer_norm_epsilon)
+            self.embed_dim, eps=config.layer_norm_epsilon
+        )
 
         # Transformer blocks
-        self.h = nn.ModuleList([
-            BloomBlock(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: BloomBlock(
+                config, cache_config, quant_config, prefix=prefix
+            ),
+            prefix=f"{prefix}.h",
+        )
 
         # Final Layer Norm
         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.hidden_size
+        )
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.word_embeddings(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.word_embeddings(input_ids)
-        hidden_states = self.word_embeddings_layernorm(hidden_states)
-        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)
+            hidden_states = self.word_embeddings_layernorm(hidden_states)
+        else:
+            assert intermediate_tensors is not None
+            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 BloomForCausalLM(nn.Module):
-
-    def __init__(
-        self,
-        config: BloomConfig,
-        quant_config: Optional[QuantizationConfig] = None,
-    ):
-        super().__init__()
-        self.config = config
-        self.quant_config = quant_config
-        self.transformer = BloomModel(config, quant_config)
-        self.lm_head_weight = self.transformer.word_embeddings.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 name == "lm_head.weight":
+            if is_pp_missing_parameter(name, self):
                 continue
-            if not name.startswith("transformer."):
-                name = "transformer." + name
             param = params_dict[name]
 
             if "query_key_value" in name:
@@ -316,12 +315,76 @@ class BloomForCausalLM(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 BloomForCausalLM(nn.Module, SupportsPP, SupportsQuant):
+    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 = BloomModel(
+            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "transformer")
+        )
+        if self.config.tie_word_embeddings:
+            self.lm_head = self.transformer.word_embeddings
+        else:
+            self.lm_head = ParallelLMHead(
+                self.config.vocab_size,
+                self.config.hidden_size,
+                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)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self, skip_prefixes=["lm_head.weight"])
+        weights = _add_transformer_prefix(weights)
+        return loader.load_weights(weights)
+
+
+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."):
+            name = "transformer." + name
+        yield name, tensor