[Model] Supporet llama3 on v0.11.0

FULL AND PIECEWISE GRAPH ENBALE

vllm_kunlun/models/llama.py

@@ -1,6 +1,31 @@
 # 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/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only LLaMA model compatible with HuggingFace weights."""
+# 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/llama/modeling_llama.py
 # Copyright 2023 The vLLM team.
@@ -24,6 +49,7 @@
 # limitations under the License.
 """Inference-only LLaMA model compatible with HuggingFace weights."""
 from collections.abc import Iterable
+from itertools import islice
 from typing import Any, Optional, Union
 
 import torch
@@ -37,20 +63,22 @@ from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm_kunlun.ops.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+from vllm_kunlun.ops.linear import (MergedColumnParallelLinear,
                                                QKVParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.rotary_embedding import get_rope
+
+from vllm_kunlun.ops.vocab_parallel_embedding import VocabParallelEmbedding
+
 from vllm.model_executor.layers.vocab_parallel_embedding import (
-    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead)
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader, maybe_remap_kv_scale_name)
-from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
-from vllm.model_executor.models.interfaces import SupportsLoRA, SupportsPP
+from vllm.model_executor.models.interfaces import SupportsEagle3, SupportsLoRA, SupportsPP
 from vllm.model_executor.models.utils import (AutoWeightsLoader, PPMissingLayer, extract_layer_index,
                     is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
@@ -68,6 +96,7 @@ class LlamaMLP(nn.Module):
         bias: bool = False,
         prefix: str = "",
         reduce_results: bool = True,
+        disable_tp: bool = False,
     ) -> None:
         super().__init__()
         self.gate_up_proj = MergedColumnParallelLinear(
@@ -75,6 +104,7 @@ class LlamaMLP(nn.Module):
             output_sizes=[intermediate_size] * 2,
             bias=bias,
             quant_config=quant_config,
+            disable_tp=disable_tp,
             prefix=f"{prefix}.gate_up_proj",
         )
         self.down_proj = RowParallelLinear(
@@ -83,6 +113,7 @@ class LlamaMLP(nn.Module):
             bias=bias,
             quant_config=quant_config,
             reduce_results=reduce_results,
+            disable_tp=disable_tp,
             prefix=f"{prefix}.down_proj",
         )
         if hidden_act != "silu":
@@ -168,20 +199,31 @@ class LlamaAttention(nn.Module):
                               rope_scaling=rope_scaling,
                               quant_config=quant_config)
 
-        if hasattr(config, "interleaved_sliding_window"):
-            interleaved_sliding_window = config.interleaved_sliding_window
-            if isinstance(interleaved_sliding_window, int):
-                sliding_window = interleaved_sliding_window
-            elif isinstance(interleaved_sliding_window, list):
-                sw_idx = layer_idx % len(interleaved_sliding_window)
-                sliding_window = interleaved_sliding_window[sw_idx]
+        sliding_window = None
+        if layer_types := getattr(config, "layer_types", None):
+            # Fix for Eagle3 compatibility:
+            # for draft models, subtract target layer count
+            # to get draft-relative layer index starting from 0
+            if hasattr(config, 'target_layer_count'):
+                # This is a draft model,
+                # adjust layer_idx to be relative to draft layers
+                effective_layer_idx = layer_idx - config.target_layer_count
             else:
-                raise ValueError(
-                    f"{type(interleaved_sliding_window)} is not supported.")
-        else:
-            sliding_window = None
+                # This is a target model, use layer_idx directly
+                effective_layer_idx = layer_idx
+            assert effective_layer_idx < len(layer_types), \
+                f"effective_layer_idx: {effective_layer_idx} \
+                is out of bounds for layer_types: {layer_types}"
 
-        self.attn = Attention(
+            is_sliding = layer_types[
+                effective_layer_idx] == "sliding_attention"
+            if is_sliding:
+                sliding_window = config.sliding_window
+
+        attn_cls = (EncoderOnlyAttention
+                    if attn_type == AttentionType.ENCODER_ONLY else Attention)
+
+        self.attn = attn_cls(
             self.num_heads,
             self.head_dim,
             self.scaling,
@@ -200,8 +242,7 @@ class LlamaAttention(nn.Module):
     ) -> torch.Tensor:
         qkv, _ = self.qkv_proj(hidden_states)
         q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
-        #TODO@hanhaowen:use kunlun ops to speed up
-        q, k = self.rotary_emb.forward_native(positions, q, k)
+        q, k = self.rotary_emb(positions, q, k)
         attn_output = self.attn(q, k, v)
         output, _ = self.o_proj(attn_output)
         return output
@@ -227,14 +268,16 @@ class LlamaAttention(nn.Module):
 
 class LlamaDecoderLayer(nn.Module):
 
-    def __init__(
-        self,
-        config: LlamaConfig,
-        cache_config: Optional[CacheConfig] = None,
-        quant_config: Optional[QuantizationConfig] = None,
-        prefix: str = "",
-    ) -> None:
+    def __init__(self,
+                 vllm_config: VllmConfig,
+                 prefix: str = "",
+                 config: Optional[LlamaConfig] = None) -> None:
         super().__init__()
+
+        config = config or vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
         self.hidden_size = config.hidden_size
         rope_theta = getattr(config, "rope_theta", 10000)
         rope_scaling = getattr(config, "rope_scaling", None)
@@ -306,6 +349,7 @@ class LlamaDecoderLayer(nn.Module):
                 hidden_states, residual)
         hidden_states = self.self_attn(positions=positions,
                                        hidden_states=hidden_states)
+
         # Fully Connected
         hidden_states, residual = self.post_attention_layernorm(
             hidden_states, residual)
@@ -313,7 +357,7 @@ class LlamaDecoderLayer(nn.Module):
         return hidden_states, residual
 
 
-# @support_torch_compile
+@support_torch_compile
 class LlamaModel(nn.Module):
 
     def __init__(self,
@@ -324,7 +368,6 @@ class LlamaModel(nn.Module):
         super().__init__()
 
         config = vllm_config.model_config.hf_config
-        cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
 
@@ -346,10 +389,7 @@ class LlamaModel(nn.Module):
             self.embed_tokens = PPMissingLayer()
         self.start_layer, self.end_layer, self.layers = make_layers(
             config.num_hidden_layers,
-            lambda prefix: layer_type(config=config,
-                                      cache_config=cache_config,
-                                      quant_config=quant_config,
-                                      prefix=prefix),
+            lambda prefix: layer_type(vllm_config=vllm_config, prefix=prefix),
             prefix=f"{prefix}.layers",
         )
         if get_pp_group().is_last_rank:
@@ -357,7 +397,7 @@ class LlamaModel(nn.Module):
         else:
             self.norm = PPMissingLayer()
 
-        self.aux_hidden_state_layers: tuple[int] = tuple()
+        self.aux_hidden_state_layers = tuple[int, ...]()
 
         self.make_empty_intermediate_tensors = (
             make_empty_intermediate_tensors_factory(
@@ -387,7 +427,7 @@ class LlamaModel(nn.Module):
 
         aux_hidden_states = []
         for idx, layer in enumerate(
-                self.layers[self.start_layer:self.end_layer]):
+                islice(self.layers, self.start_layer, self.end_layer)):
             if idx in self.aux_hidden_state_layers:
                 aux_hidden_states.append(hidden_states + residual)
             hidden_states, residual = layer(positions, hidden_states, residual)
@@ -471,7 +511,7 @@ class LlamaModel(nn.Module):
         return loaded_params
 
 
-class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
     packed_modules_mapping = {
         "qkv_proj": ["q_proj", "k_proj", "v_proj"],
         "gate_up_proj": ["gate_proj", "up_proj"]
@@ -557,10 +597,10 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         self.make_empty_intermediate_tensors = (
             self.model.make_empty_intermediate_tensors)
 
-    def set_aux_hidden_state_layers(self, layers: tuple[int]) -> None:
+    def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
         self.model.aux_hidden_state_layers = layers
 
-    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int]:
+    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
         num_layers = len(self.model.layers)
         return (2, num_layers // 2, num_layers - 3)
 
@@ -589,10 +629,8 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
     ) -> Optional[torch.Tensor]:
-        logits = self.logits_processor(self.lm_head, hidden_states,
-                                       sampling_metadata)
+        logits = self.logits_processor(self.lm_head, hidden_states)
         return logits
 
     def load_weights(self, weights: Iterable[tuple[str,
@@ -614,9 +652,8 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         loaded_weight: torch.Tensor,
     ) -> tuple[str, torch.Tensor]:
 
-        def permute(w: torch.Tensor, n_heads: int):
+        def permute(w: torch.Tensor, n_heads: int, attn_out: int):
             attn_in = self.config.head_dim * n_heads
-            attn_out = self.config.hidden_size
 
             return w.view(n_heads, attn_in // n_heads // 2, 2,
                           attn_out).transpose(1, 2).reshape(attn_in, attn_out)
@@ -625,12 +662,24 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         modules = name.split(".")
 
         # rotary embeds should be sliced
+        # If using quantized model in mistral format,
+        # quantization scales (qscale_weight) also need to be sliced
         if "wk" in modules and modules[-1] == "weight":
             loaded_weight = permute(loaded_weight,
-                                    self.config.num_key_value_heads)
+                                    self.config.num_key_value_heads,
+                                    self.config.hidden_size)
+        elif "wk" in modules and modules[
+                -1] == "qscale_weight" and loaded_weight.numel() > 1:
+            loaded_weight = permute(loaded_weight,
+                                    self.config.num_key_value_heads, 1)
         elif "wq" in modules and modules[-1] == "weight":
             loaded_weight = permute(loaded_weight,
-                                    self.config.num_attention_heads)
+                                    self.config.num_attention_heads,
+                                    self.config.hidden_size)
+        elif "wq" in modules and modules[
+                -1] == "qscale_weight" and loaded_weight.numel() > 1:
+            loaded_weight = permute(loaded_weight,
+                                    self.config.num_attention_heads, 1)
 
         num_modules = len(modules)
         for i in range(num_modules):
@@ -646,3 +695,4 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                 name = name.replace(item, mapping[item])
 
         return name, loaded_weight
+