[gpt-oss] Add gpt-oss bf16 support

2025-08-13 21:25:57 +08:00
parent 5d2e7edf78
commit 17ea2ec6aa
1232 changed files with 777 additions and 36 deletions
--- a/vllm/model_executor/model_loader/neuron.py
+++ b/vllm/model_executor/model_loader/neuron.py
@@ -0,0 +1,476 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Utilities for selecting and loading Neuron models in transformers-neuronx
+framework."""
+import ast
+import copy
+import importlib
+import os
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from transformers import PretrainedConfig
+
+from vllm.config import (ModelConfig, ParallelConfig, SchedulerConfig,
+                         SpeculativeConfig)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import get_quantization_config
+from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import (CompletionSequenceGroupOutput, Logprob,
+                           SequenceOutput)
+
+TORCH_DTYPE_TO_NEURON_AMP = {
+    "auto": "f32",
+    "half": "f16",
+    "float16": "f16",
+    "bfloat16": "bf16",
+    "float": "f32",
+    "float32": "f32",
+    torch.float16: "f16",
+    torch.bfloat16: "bf16",
+    torch.float32: "f32",
+}
+
+# Models supported by Neuron.
+_NEURON_SUPPORTED_MODELS: dict[str, tuple[str, str, str]] = {
+    "LlamaForCausalLM": ("transformers_neuronx.llama.model",
+                         "LlamaForSampling", "LlamaForCausalLM"),
+    "MistralForCausalLM": ("transformers_neuronx.mistral.model",
+                           "MistralForSampling", "MistralForCausalLM")
+}
+
+
+class NeuronCausalLM(nn.Module):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 on_device_sampling_disabled: bool = False) -> None:
+        super().__init__()
+        self.config = config
+        self.logits_processor = LogitsProcessor(config.vocab_size,
+                                                logits_as_input=True)
+
+        self.on_device_sampling_disabled = on_device_sampling_disabled
+        if self.on_device_sampling_disabled:
+            # Use default sampler
+            self.sampler = Sampler()
+
+        # Lazy initialized
+        self.model: nn.Module
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        input_block_ids: torch.Tensor,
+    ) -> torch.Tensor:
+        logits = self.model(input_ids,
+                            cache_ids=positions,
+                            start_ids=input_block_ids)
+        return logits
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(None, hidden_states, sampling_metadata)
+        return logits
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+
+        if self.on_device_sampling_disabled:
+            next_tokens = self.sampler(logits, sampling_metadata)
+            return next_tokens
+
+        # On-device sampling outputs the token ids directly.
+        sampled_token_ids = logits.flatten()
+        next_tokens = []
+        sample_idx = 0
+        for seq_group in sampling_metadata.seq_groups:
+            samples = []
+            for seq_id in seq_group.seq_ids:
+                token_id = sampled_token_ids[sample_idx].item()
+                samples.append(
+                    SequenceOutput(parent_seq_id=seq_id,
+                                   output_token=token_id,
+                                   logprobs={token_id: Logprob(token_id)}))
+                sample_idx += 1
+            next_tokens.append(
+                CompletionSequenceGroupOutput(samples=samples,
+                                              prompt_logprobs=None))
+
+        return SamplerOutput(outputs=next_tokens)
+
+    def load_weights(self, model_name_or_path: str, **kwargs):
+        arch = _get_model_architecture(self.config)
+        neuronx_module_path, neuronx_model_cls_name, hf_model_cls_name = (
+            _NEURON_SUPPORTED_MODELS[arch])
+        neuronx_module = importlib.import_module(neuronx_module_path)
+        neuronx_model_cls = getattr(neuronx_module, neuronx_model_cls_name)
+
+        self.model = neuronx_model_cls.from_pretrained(model_name_or_path,
+                                                       **kwargs)
+        self.model.to_neuron()
+
+
+class NeuronSpeculationCausalLM(nn.Module):
+    """A Neuron-optimized causal language model with speculative decoding."""
+
+    SPECULATION_TERMINATION_ID = -1
+
+    def __init__(self, speculation_model) -> None:
+        super().__init__()
+        self.model = speculation_model
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        input_block_ids: torch.Tensor,
+    ) -> torch.Tensor:
+        tokens, counts = self.model.speculative_iteration(
+            input_ids, positions, input_block_ids)
+
+        # Mark the end of accepted speculative tokens for each sequence with the
+        # speculation termination id.
+        batch_size, steps = tokens.shape
+        mask = torch.arange(steps).expand(batch_size, -1) >= counts
+        tokens[mask] = self.SPECULATION_TERMINATION_ID
+
+        return tokens
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[list[SamplerOutput]]:
+        batch_size, num_steps = logits.shape
+        seq_ids = [
+            seq_id for sg in sampling_metadata.seq_groups
+            for seq_id in sg.seq_ids
+        ]
+        # Organize input tensors by step instead of by sequence.
+        accepted_token_ids_by_step = logits.transpose(0, 1)
+        accepted_token_ids_by_step = accepted_token_ids_by_step.tolist()
+
+        sampler_output_list = []
+        for step_index in range(num_steps):
+            if all(token_id == self.SPECULATION_TERMINATION_ID
+                   for token_id in accepted_token_ids_by_step[step_index]):
+                break
+            step_output_token_ids = []
+            for sequence_index in range(batch_size):
+                token_id = accepted_token_ids_by_step[step_index][
+                    sequence_index]
+                step_output_token_ids.append(
+                    CompletionSequenceGroupOutput(samples=[
+                        SequenceOutput(parent_seq_id=seq_ids[sequence_index],
+                                       output_token=token_id,
+                                       logprobs={token_id: Logprob(token_id)})
+                    ],
+                                                  prompt_logprobs=None))
+            sampler_output_list.append(
+                SamplerOutput(outputs=step_output_token_ids))
+        return sampler_output_list
+
+
+def _get_model_architecture(config: PretrainedConfig) -> str:
+    architectures = getattr(config, "architectures", [])
+    for arch in architectures:
+        if arch in _NEURON_SUPPORTED_MODELS:
+            return arch
+    raise ValueError(
+        f"Model architectures {architectures} are not supported on Neuron "
+        f"for now. Supported architectures: "
+        f"{list(_NEURON_SUPPORTED_MODELS.keys())}")
+
+
+def _get_buckets(env: str, default_value: list[int]) -> list[int]:
+    env_value = os.getenv(env)
+    if env_value is None:
+        return default_value
+    buckets_remove_empty = filter(
+        lambda x: x is not None and len(x.strip()) > 0, env_value.split(","))
+    buckets_int = map(int, buckets_remove_empty)
+    buckets_list = list(buckets_int)
+    return buckets_list
+
+
+def _get_default_neuron_config(model_config: ModelConfig,
+                               parallel_config: ParallelConfig,
+                               scheduler_config: SchedulerConfig):
+    """Generate a neuron config based on vllm config args."""
+    from transformers_neuronx.config import ContinuousBatchingConfig
+    from transformers_neuronx.constants import LAYOUT_BSH
+
+    continuous_batching_config = ContinuousBatchingConfig(
+        batch_size_for_shared_caches=scheduler_config.max_num_seqs)
+    quant_config = dict(
+        dequant_dtype=TORCH_DTYPE_TO_NEURON_AMP[model_config.dtype],
+        quantize_method="vector_dynamic")
+    neuron_quantization_config_builder = lambda quant: get_quantization_config(
+        quant).from_config(quant_config).get_quant_method(None, "")
+    # TODO: Add Paged attention config to the default neuron arguments.
+    default_neuron_args = dict(
+        collectives_layout=LAYOUT_BSH,
+        attention_layout=LAYOUT_BSH,
+        fuse_qkv=True,
+        quant=neuron_quantization_config_builder(model_config.quantization)
+        if model_config.quantization else None,
+        continuous_batching=continuous_batching_config,
+        weight_tiling=bool(model_config.quantization),
+        on_device_generation=_get_neuron_on_device_generation_config(
+            model_config))
+    return default_neuron_args
+
+
+def _get_default_neuron_config_for_speculation(
+        model_config: ModelConfig, parallel_config: ParallelConfig,
+        scheduler_config: SchedulerConfig):
+    """Generate a neuron config for speculative decoding based on
+    vllm config args."""
+    from transformers_neuronx.config import ContinuousBatchingConfig
+    from transformers_neuronx.constants import LAYOUT_BSH
+
+    continuous_batching_config = ContinuousBatchingConfig(
+        batch_size_for_shared_caches=scheduler_config.max_num_seqs)
+
+    default_neuron_args = dict(collectives_layout=LAYOUT_BSH,
+                               attention_layout=LAYOUT_BSH,
+                               fuse_qkv=True,
+                               on_device_embedding=True,
+                               continuous_batching=continuous_batching_config,
+                               on_device_generation=copy.deepcopy(
+                                   model_config.neuron_sampling_params))
+    return default_neuron_args
+
+
+def _get_neuron_on_device_generation_config(model_config: ModelConfig):
+    if not _is_neuron_on_device_sampling_disabled(model_config):
+        return copy.deepcopy(model_config.neuron_sampling_params)
+    return None
+
+
+def _is_neuron_on_device_sampling_disabled(model_config: ModelConfig) -> bool:
+    return not getattr(model_config, "neuron_sampling_params", None)
+
+
+def _get_neuron_config_after_override(default_neuron_config,
+                                      overridden_neuron_config):
+    from transformers_neuronx.config import (ContinuousBatchingConfig,
+                                             GenerationConfig,
+                                             KVCacheQuantizationConfig,
+                                             NeuronConfig, QuantizationConfig,
+                                             SparseAttnConfig)
+
+    sparse_attn = overridden_neuron_config.pop("sparse_attn", {})
+    if sparse_attn:
+        overridden_neuron_config["sparse_attn"] = SparseAttnConfig(
+            **sparse_attn)
+
+    kv_cache_quant = overridden_neuron_config.pop("kv_cache_quant", {})
+    if kv_cache_quant:
+        overridden_neuron_config["kv_cache_quant"] = KVCacheQuantizationConfig(
+            **kv_cache_quant)
+
+    continuous_batching = overridden_neuron_config.pop("continuous_batching",
+                                                       {})
+    if continuous_batching:
+        overridden_neuron_config[
+            "continuous_batching"] = ContinuousBatchingConfig(
+                **continuous_batching)
+
+    quant = overridden_neuron_config.pop("quant", {})
+    if quant:
+        overridden_neuron_config["quant"] = QuantizationConfig(**quant)
+
+    on_device_generation = overridden_neuron_config.pop(
+        "on_device_generation", {})
+    if on_device_generation:
+        overridden_neuron_config["on_device_generation"] = GenerationConfig(
+            **on_device_generation)
+    default_neuron_config.update(overridden_neuron_config)
+    return NeuronConfig(**default_neuron_config)
+
+
+def get_neuron_model(model_config: ModelConfig,
+                     parallel_config: ParallelConfig,
+                     scheduler_config: SchedulerConfig) -> nn.Module:
+    """Initializes a neuron-optimized model for inference."""
+    # Create a model instance.
+    model = NeuronCausalLM(
+        model_config.hf_config,
+        _is_neuron_on_device_sampling_disabled(model_config))
+
+    default_neuron_config_args = _get_default_neuron_config(
+        model_config, parallel_config, scheduler_config)
+
+    neuron_config = _get_neuron_config_after_override(
+        default_neuron_config_args, model_config.override_neuron_config)
+
+    context_length_estimates = _get_buckets("NEURON_CONTEXT_LENGTH_BUCKETS",
+                                            [scheduler_config.max_model_len])
+    n_positions = _get_buckets("NEURON_TOKEN_GEN_BUCKETS",
+                               [scheduler_config.max_model_len])
+
+    model.load_weights(model_config.model,
+                       tp_degree=parallel_config.tensor_parallel_size,
+                       amp=TORCH_DTYPE_TO_NEURON_AMP[model_config.dtype],
+                       neuron_config=neuron_config,
+                       context_length_estimate=context_length_estimates,
+                       n_positions=n_positions,
+                       batch_size=scheduler_config.max_num_seqs)
+
+    return model.eval()
+
+
+def get_neuron_speculation_model(model_config: ModelConfig,
+                                 parallel_config: ParallelConfig,
+                                 scheduler_config: SchedulerConfig,
+                                 speculation_config: SpeculativeConfig):
+    """Initializes a neuron-optimized speculation model for inference.
+
+    This method is only applicable for speculation with a standalone draft model
+    """
+    from transformers_neuronx.fused_speculation import FusedSpeculativeDecoder
+
+    # For Eagle SD, we need to pass in additional parameters in neuron config.
+    is_eagle = getattr(speculation_config.draft_model_config.hf_config,
+                       "is_eagle", False)
+
+    # Create target model instance.
+    target_model = NeuronCausalLM(model_config.hf_config)
+
+    default_neuron_config_args = _get_default_neuron_config_for_speculation(
+        model_config, parallel_config, scheduler_config)
+    if is_eagle:
+        default_neuron_config_args['is_eagle_target'] = True
+
+    neuron_config = _get_neuron_config_after_override(
+        default_neuron_config_args, model_config.override_neuron_config)
+
+    context_length_estimates = _get_buckets("NEURON_CONTEXT_LENGTH_BUCKETS",
+                                            [scheduler_config.max_model_len])
+    n_positions = _get_buckets("NEURON_TOKEN_GEN_BUCKETS",
+                               [scheduler_config.max_model_len])
+
+    target_model.load_weights(
+        model_config.model,
+        tp_degree=parallel_config.tensor_parallel_size,
+        amp=TORCH_DTYPE_TO_NEURON_AMP[model_config.dtype],
+        neuron_config=neuron_config,
+        context_length_estimate=context_length_estimates,
+        n_positions=n_positions,
+        batch_size=scheduler_config.max_num_seqs)
+
+    target_model.eval()
+
+    # Create draft model instance.
+    draft_model = NeuronCausalLM(
+        speculation_config.draft_model_config.hf_config)
+
+    default_draft_neuron_config_args = (
+        _get_default_neuron_config_for_speculation(
+            speculation_config.draft_model_config, parallel_config,
+            scheduler_config))
+    if is_eagle:
+        default_draft_neuron_config_args['is_eagle_draft'] = True
+        default_draft_neuron_config_args['has_pre_attention_norm'] = False
+
+    draft_neuron_config = _get_neuron_config_after_override(
+        default_draft_neuron_config_args,
+        speculation_config.draft_model_config.override_neuron_config)
+
+    draft_model.load_weights(speculation_config.draft_model_config.model,
+                             tp_degree=speculation_config.
+                             draft_parallel_config.tensor_parallel_size,
+                             amp=TORCH_DTYPE_TO_NEURON_AMP[
+                                 speculation_config.draft_model_config.dtype],
+                             neuron_config=draft_neuron_config,
+                             context_length_estimate=context_length_estimates,
+                             n_positions=n_positions,
+                             batch_size=scheduler_config.max_num_seqs)
+
+    draft_model.eval()
+
+    num_speculative_tokens = speculation_config.num_speculative_tokens
+    # Create speculation model instance.
+    speculation_model = FusedSpeculativeDecoder(draft_model.model,
+                                                target_model.model,
+                                                num_speculative_tokens)
+    speculation_model.to_neuron()
+
+    return NeuronSpeculationCausalLM(speculation_model)
+
+
+def get_neuron_eagle_speculation_model(model_config: ModelConfig,
+                                       parallel_config: ParallelConfig,
+                                       scheduler_config: SchedulerConfig,
+                                       speculation_config: SpeculativeConfig):
+    """Initializes a neuron-optimized EAGLE speculation model for inference."""
+    from transformers_neuronx.eagle_speculation import EagleSpeculativeDecoder
+
+    # Create target model instance.
+    target_model = NeuronCausalLM(model_config.hf_config)
+
+    default_neuron_config_args = _get_default_neuron_config_for_speculation(
+        model_config, parallel_config, scheduler_config)
+    default_neuron_config_args['is_eagle_target'] = True
+    neuron_config = _get_neuron_config_after_override(
+        default_neuron_config_args, model_config.override_neuron_config)
+
+    context_length_estimates = _get_buckets("NEURON_CONTEXT_LENGTH_BUCKETS",
+                                            [scheduler_config.max_model_len])
+    n_positions = _get_buckets("NEURON_TOKEN_GEN_BUCKETS",
+                               [scheduler_config.max_model_len])
+
+    target_model.load_weights(
+        model_config.model,
+        tp_degree=parallel_config.tensor_parallel_size,
+        amp=TORCH_DTYPE_TO_NEURON_AMP[model_config.dtype],
+        neuron_config=neuron_config,
+        context_length_estimate=context_length_estimates,
+        n_positions=n_positions,
+        batch_size=scheduler_config.max_num_seqs)
+
+    target_model.eval()
+
+    # Create draft model instance.
+    draft_model = NeuronCausalLM(
+        speculation_config.draft_model_config.hf_config)
+
+    default_draft_neuron_config_args = (
+        _get_default_neuron_config_for_speculation(
+            speculation_config.draft_model_config, parallel_config,
+            scheduler_config))
+    default_draft_neuron_config_args['is_eagle_draft'] = True
+    default_draft_neuron_config_args['has_pre_attention_norm'] = False
+    draft_neuron_config = _get_neuron_config_after_override(
+        default_draft_neuron_config_args,
+        speculation_config.draft_model_config.override_neuron_config)
+
+    draft_model.load_weights(speculation_config.draft_model_config.model,
+                             tp_degree=speculation_config.
+                             draft_parallel_config.tensor_parallel_size,
+                             amp=TORCH_DTYPE_TO_NEURON_AMP[
+                                 speculation_config.draft_model_config.dtype],
+                             neuron_config=draft_neuron_config,
+                             context_length_estimate=context_length_estimates,
+                             n_positions=n_positions,
+                             batch_size=scheduler_config.max_num_seqs)
+
+    draft_model.eval()
+
+    token_tree: dict[int, list[int]] = ast.literal_eval(
+        speculation_config.speculative_token_tree)
+
+    speculation_model = EagleSpeculativeDecoder(draft_model.model,
+                                                target_model.model,
+                                                token_tree=token_tree)
+    speculation_model.to_neuron()
+
+    return NeuronSpeculationCausalLM(speculation_model)