chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,149 @@
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"""
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This file specifies how MLC's Deepseek parameter maps from other formats, for example HuggingFace
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PyTorch, HuggingFace safetensors.
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"""
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import functools
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import numpy as np
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from mlc_llm.loader import ExternMapping
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from mlc_llm.quantization import Quantization
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from .deepseek_model import DeepseekConfig, DeepseekForCausalLM
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def huggingface(model_config: DeepseekConfig, quantization: Quantization) -> ExternMapping:
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"""Returns a parameter mapping that maps from the names of MLC LLM parameters to
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the names of HuggingFace PyTorch parameters.
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Parameters
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----------
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model_config : MiniCPMConfig
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The configuration of the MiniCPM model.
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quantization : Quantization
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The quantization configuration.
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Returns
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-------
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param_map : ExternMapping
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The parameter mapping from MLC to HuggingFace PyTorch.
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"""
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model = DeepseekForCausalLM(model_config)
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if quantization is not None:
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model.to(quantization.model_dtype)
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_, _named_params, _ = model.export_tvm(
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spec=model.get_default_spec(),
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allow_extern=True,
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)
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named_parameters = dict(_named_params)
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mapping = ExternMapping()
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for i in range(model_config.num_hidden_layers):
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# map attention weight
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attn = f"model.layers.{i}.self_attn"
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for weight_type in ["weight"]:
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mlc_name = f"{attn}.wqkv_pack.{weight_type}"
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mlc_param = named_parameters[mlc_name]
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mapping.add_mapping(
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mlc_name,
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[
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f"{attn}.q_proj.{weight_type}",
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f"{attn}.k_proj.{weight_type}",
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f"{attn}.v_proj.{weight_type}",
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],
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functools.partial(
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lambda q, k, v, dtype: np.concatenate([q, k, v], axis=0).astype(dtype),
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dtype=mlc_param.dtype,
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),
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)
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for i in range(model_config.num_hidden_layers):
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if i >= model_config.first_k_dense_replace and i % model_config.moe_layer_freq == 0:
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# map mlp shared expert weight
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mlp = f"model.layers.{i}.mlp"
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shared_expert = f"{mlp}.shared_experts"
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mlc_name = f"{shared_expert}.gate_up_proj.weight"
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mlc_param = named_parameters[mlc_name]
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mapping.add_mapping(
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mlc_name,
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[
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f"{shared_expert}.gate_proj.weight",
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f"{shared_expert}.up_proj.weight",
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],
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functools.partial(
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lambda gate, up, dtype: np.concatenate([gate, up], axis=0).astype(dtype),
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dtype=mlc_param.dtype,
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),
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)
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# map mlp moe gate and up weight
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mlc_name = f"{mlp}.moe_gate_up_proj.weight"
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def combine_expert_gate_up(*hf_params, dtype):
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stack = []
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for i in range(0, len(hf_params), 2):
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stack.append(np.concatenate([hf_params[i], hf_params[i + 1]], axis=0))
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return np.stack(stack, axis=0).astype(dtype)
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mapping.add_mapping(
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mlc_name,
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functools.reduce(
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lambda a, b: a + b,
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[
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[
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f"{mlp}.experts.{expert_id}.gate_proj.weight",
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f"{mlp}.experts.{expert_id}.up_proj.weight",
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]
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for expert_id in range(model_config.n_routed_experts)
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],
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),
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functools.partial(
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combine_expert_gate_up,
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dtype=mlc_param.dtype,
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),
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)
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# map mlp moe gate and up weight
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mlc_name = f"{mlp}.moe_down_proj.weight"
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mlc_param = named_parameters[mlc_name]
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mapping.add_mapping(
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mlc_name,
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[
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f"{mlp}.experts.{expert_id}.down_proj.weight"
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for expert_id in range(model_config.n_routed_experts)
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],
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functools.partial(
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lambda *hf_params, dtype: np.stack(hf_params, axis=0).astype(dtype),
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dtype=mlc_param.dtype,
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),
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)
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else:
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# map mlp weight
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mlp = f"model.layers.{i}.mlp"
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mlc_name = f"{mlp}.gate_up_proj.weight"
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mlc_param = named_parameters[mlc_name]
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mapping.add_mapping(
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mlc_name,
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[
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f"{mlp}.gate_proj.weight",
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f"{mlp}.up_proj.weight",
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],
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functools.partial(
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lambda gate, up, dtype: np.concatenate([gate, up], axis=0).astype(dtype),
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dtype=mlc_param.dtype,
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),
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)
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for mlc_name, mlc_param in named_parameters.items():
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if mlc_name not in mapping.param_map:
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mapping.add_mapping(
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mlc_name,
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[mlc_name],
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functools.partial(
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lambda x, dtype: x.astype(dtype),
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dtype=mlc_param.dtype,
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),
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)
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return mapping
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@@ -0,0 +1,516 @@
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"""
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Implementation for Deepseek architecture.
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"""
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import dataclasses
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from functools import partial
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from typing import Any, Dict, Optional # noqa: UP035
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from tvm import tirx
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from tvm.relax.frontend import nn
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from tvm.relax.frontend.nn import Tensor, op
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from mlc_llm import op as op_ext
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from mlc_llm.model.model_utils import index_last_token
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from mlc_llm.nn import PagedKVCache, RopeMode
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from mlc_llm.nn.expert import MixtralExperts
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from mlc_llm.support import logging
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from mlc_llm.support import tensor_parallel as tp
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from mlc_llm.support.config import ConfigBase
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from mlc_llm.support.style import bold
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logger = logging.getLogger(__name__)
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@dataclasses.dataclass
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class DeepseekConfig(ConfigBase):
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"""Configuration of the Deepseek model."""
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vocab_size: int
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hidden_size: int
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intermediate_size: int
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moe_intermediate_size: int
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num_hidden_layers: int
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num_attention_heads: int
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num_key_value_heads: int
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n_shared_experts: int
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n_routed_experts: int
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moe_layer_freq: int
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first_k_dense_replace: int
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hidden_act: str
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norm_topk_prob: bool
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attention_bias: bool
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rms_norm_eps: float
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use_cache: bool
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bos_token_id: int
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eos_token_id: int
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tie_word_embeddings: bool = False
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rope_theta: int = 10000
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context_window_size: int = 0
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prefill_chunk_size: int = 0
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tensor_parallel_shards: int = 1
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head_dim: int = 0
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max_batch_size: int = 1
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num_experts_per_tok: int = 0
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kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict) # noqa: UP006
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def __post_init__(self):
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if self.context_window_size == 0:
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for name in ["max_position_embeddings", "max_sequence_length"]:
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if name in self.kwargs:
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self.context_window_size = self.kwargs.pop(name)
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logger.info(
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"%s not found in config.json. Falling back to %s (%d)",
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bold("context_window_size"),
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bold(name),
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self.context_window_size,
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)
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break
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else:
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raise ValueError(
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"Unable to determine the maximum sequence length, because none of "
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"`context_window_size`, `max_position_embeddings` or `max_sequence_length` is "
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"provided in `config.json`."
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)
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if self.head_dim == 0:
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self.head_dim = self.hidden_size // self.num_attention_heads
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assert self.head_dim * self.num_attention_heads == self.hidden_size
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if self.prefill_chunk_size == 0:
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logger.info(
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"%s defaults to %d",
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bold("prefill_chunk_size"),
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min(self.context_window_size, 8192),
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)
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self.prefill_chunk_size = min(self.context_window_size, 8192)
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elif self.prefill_chunk_size > self.context_window_size:
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logger.info(
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"Overriding %s from %d to %d",
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bold("prefill_chunk_size"),
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self.prefill_chunk_size,
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min(self.context_window_size, 8192),
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)
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self.prefill_chunk_size = min(self.context_window_size, 8192)
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class DeepseekAttention(nn.Module):
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def __init__(self, config: DeepseekConfig):
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super().__init__() # Make sure to call the parent class constructor
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self.hidden_size = config.hidden_size
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self.rope_theta = config.rope_theta
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self.tensor_parallel_shards = config.tensor_parallel_shards
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if config.num_attention_heads % config.tensor_parallel_shards != 0:
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raise ValueError(
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f"Cannot split {config.num_attention_heads} attention heads "
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f"evenly to {config.tensor_parallel_shards} GPUs."
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)
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self.attention_bias = config.attention_bias
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self.num_heads = config.num_attention_heads // self.tensor_parallel_shards
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self.num_key_value_heads = config.num_key_value_heads // self.tensor_parallel_shards
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self.num_key_value_groups = self.num_heads // self.num_key_value_heads
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self.head_dim = config.head_dim
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self.max_position_embeddings = config.context_window_size
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self.wqkv_pack = nn.Linear(
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in_features=self.hidden_size,
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out_features=(self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
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bias=self.attention_bias,
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)
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self.o_proj = nn.Linear(
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self.num_heads * self.head_dim, self.hidden_size, bias=self.attention_bias
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)
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def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
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d, h_q, h_kv = self.head_dim, self.num_heads, self.num_key_value_heads
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b, s, _ = hidden_states.shape
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qkv = self.wqkv_pack(hidden_states)
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qkv = op.reshape(qkv, (b, s, h_q + h_kv + h_kv, d))
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output = op.reshape(
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paged_kv_cache.attention_with_fused_qkv(
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layer_id, qkv, self.num_heads, sm_scale=self.head_dim**-0.5
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),
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(b, s, h_q * d),
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)
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attn_output = self.o_proj(output)
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return attn_output
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ACT2FN = {
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"gelu": partial(nn.gelu, approximate=False),
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"relu": nn.relu,
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"silu": nn.silu,
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"swish": nn.silu,
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"gelu_new": partial(nn.gelu, approximate=True),
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}
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class DeepseekMLP(nn.Module):
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def __init__(self, config: DeepseekConfig, intermediate_size=None):
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self.hidden_size = config.hidden_size
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if config.intermediate_size % config.tensor_parallel_shards != 0:
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raise ValueError(
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f"Cannot split MLP intermediate size {config.intermediate_size} "
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f"evenly to {config.tensor_parallel_shards} GPUs."
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)
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self.intermediate_size = (
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config.intermediate_size if intermediate_size is None else intermediate_size
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) // config.tensor_parallel_shards
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self.gate_up_proj = nn.Linear(self.hidden_size, 2 * self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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self.act_fn = ACT2FN[config.hidden_act]
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def forward(self, x: Tensor):
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concat_x1_x2 = self.gate_up_proj(x)
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x1, x2 = op.split(concat_x1_x2, 2, axis=-1)
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return self.down_proj(op.silu(x1) * x2)
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class DeepseekMoE(nn.Module):
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def __init__(self, config: DeepseekConfig):
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self.num_local_experts = config.n_routed_experts
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self.num_experts_per_tok = config.num_experts_per_tok
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self.gate = nn.Linear(config.hidden_size, config.n_routed_experts, bias=False)
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self.norm_topk_prob = config.norm_topk_prob
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self.moe_intermediate_size = config.moe_intermediate_size // config.tensor_parallel_shards
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self.moe_gate_up_proj = MixtralExperts(
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self.num_local_experts,
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in_features=config.hidden_size,
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out_features=2 * self.moe_intermediate_size,
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tensor_parallel_shards=config.tensor_parallel_shards,
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)
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self.moe_down_proj = MixtralExperts(
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self.num_local_experts,
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in_features=self.moe_intermediate_size,
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out_features=config.hidden_size,
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tensor_parallel_shards=config.tensor_parallel_shards,
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)
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self.dtype = "float32"
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if config.n_shared_experts is not None:
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intermediate_size = self.moe_intermediate_size * config.n_shared_experts
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self.shared_experts = DeepseekMLP(config, intermediate_size=intermediate_size)
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def forward(self, x: Tensor):
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def _expert_forward(x: Tensor, indptr: Tensor):
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x1_x3 = self.moe_gate_up_proj(x, indptr)
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x1, x3 = op.split(x1_x3, indices_or_sections=2, axis=-1)
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x = self.moe_down_proj(op.silu(x1) * x3, indptr)
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return x
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experts_per_tok = self.num_experts_per_tok
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num_experts = self.num_local_experts
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b, s, h = x.shape
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num_tokens = b * s
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x = op.reshape(x, (num_tokens, h))
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gate = self.gate(x) # (b * s, num_routed_experts)
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expert_weights, expert_indices = op_ext.moe_misc.gating_softmax_topk(
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gate, experts_per_tok, norm_topk_prob=self.norm_topk_prob
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)
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if num_tokens == 1:
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# x: [num_tokens * experts_per_tok, hidden_size]
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moe_hidden_states = _expert_forward(x, expert_indices)
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else:
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# cumsum: [num_tokens * local_experts]
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cumsum = op_ext.moe_misc.moe_cumsum(expert_indices, num_experts)
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# indices: [num_tokens * experts_per_tok]
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reverse_indices, token_indices = op_ext.moe_misc.get_indices(cumsum, expert_indices)
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# indptr: [num_local_experts + 1]
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indptr = op_ext.moe_misc.get_indptr(
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cumsum, num_experts, num_tokens, inclusive=False, out_dtype="int32"
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)
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# x: [num_tokens * experts_per_tok, hidden_size]
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moe_hidden_states = op.take(x, token_indices, axis=0)
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moe_hidden_states = _expert_forward(moe_hidden_states, indptr)
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moe_hidden_states = op_ext.moe_misc.scatter_output(moe_hidden_states, reverse_indices)
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# moe_hidden_states: [num_tokens, experts_per_tok, hidden_size]
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expert_weights = expert_weights.reshape(num_tokens, experts_per_tok, 1)
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moe_hidden_states = (
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moe_hidden_states.reshape(num_tokens, experts_per_tok, h) * expert_weights
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)
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# moe_hidden_states: [num_tokens, hidden_size]
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moe_hidden_states = op_ext.moe_misc.moe_sum(moe_hidden_states, dim=1)
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shared_expert_hidden_states = self.shared_experts(x)
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final_hidden_states = moe_hidden_states + shared_expert_hidden_states
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final_hidden_states = op.reshape(final_hidden_states, (b, s, h))
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return final_hidden_states
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class DeepseekDecoderLayer(nn.Module):
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def __init__(self, config: DeepseekConfig, layer_idx: int):
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self.hidden_size = config.hidden_size
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self.num_hidden_layers = config.num_hidden_layers
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self.self_attn = DeepseekAttention(config)
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self.num_experts = config.n_routed_experts
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self.mlp = (
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DeepseekMoE(config)
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if (
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config.n_routed_experts is not None
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and layer_idx >= config.first_k_dense_replace
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and layer_idx % config.moe_layer_freq == 0
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)
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else DeepseekMLP(config)
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)
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self.input_layernorm = nn.RMSNorm(config.hidden_size, -1, config.rms_norm_eps, bias=False)
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self.post_attention_layernorm = nn.RMSNorm(
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config.hidden_size, -1, config.rms_norm_eps, bias=False
|
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)
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def _set_tp():
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def _set(layer, hint):
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layer.attrs["shard_strategy"] = hint
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hd = config.head_dim
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q = self.self_attn.num_heads * hd
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k = self.self_attn.num_key_value_heads * hd
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v = self.self_attn.num_key_value_heads * hd
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||||
|
||||
if (
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||||
config.n_routed_experts is not None
|
||||
and layer_idx >= config.first_k_dense_replace
|
||||
and layer_idx % config.moe_layer_freq == 0
|
||||
):
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i = self.mlp.moe_intermediate_size
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else:
|
||||
i = self.mlp.intermediate_size
|
||||
_set(
|
||||
self.self_attn.wqkv_pack.weight,
|
||||
tp.ShardSingleDim("_shard_qkv_weight", dim=0, segs=[q, k, v]),
|
||||
)
|
||||
_set(self.self_attn.o_proj.weight, tp.ShardSingleDim("_shard_o", dim=1))
|
||||
|
||||
if (
|
||||
config.n_routed_experts is not None
|
||||
and layer_idx >= config.first_k_dense_replace
|
||||
and layer_idx % config.moe_layer_freq == 0
|
||||
):
|
||||
_set(
|
||||
self.mlp.moe_gate_up_proj.weight,
|
||||
tp.ShardSingleDim("_shard_mlp_up", segs=[i, i], dim=1),
|
||||
)
|
||||
_set(
|
||||
self.mlp.moe_down_proj.weight,
|
||||
tp.ShardSingleDim("_shard_mlp_down", dim=2),
|
||||
)
|
||||
|
||||
else:
|
||||
_set(
|
||||
self.mlp.gate_up_proj.weight,
|
||||
tp.ShardSingleDim("_shard_mlp_up", segs=[i, i], dim=0),
|
||||
)
|
||||
_set(
|
||||
self.mlp.down_proj.weight,
|
||||
tp.ShardSingleDim("_shard_mlp_down", dim=1),
|
||||
)
|
||||
|
||||
self.tensor_parallel_shards = config.tensor_parallel_shards
|
||||
_set_tp()
|
||||
|
||||
def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
|
||||
out = self.input_layernorm(hidden_states)
|
||||
out = self.self_attn(out, paged_kv_cache, layer_id)
|
||||
hidden_states = self._apply_residual(out, residual=hidden_states)
|
||||
out = self.post_attention_layernorm(hidden_states)
|
||||
out = self.mlp(out)
|
||||
hidden_states = self._apply_residual(out, residual=hidden_states)
|
||||
return hidden_states
|
||||
|
||||
def _apply_residual(self, out, residual):
|
||||
if self.tensor_parallel_shards > 1:
|
||||
return op.ccl_allreduce(out, "sum") + residual
|
||||
return out + residual
|
||||
|
||||
|
||||
class DeepseekModel(nn.Module):
|
||||
def __init__(self, config: DeepseekConfig):
|
||||
assert config.hidden_size % config.num_attention_heads == 0
|
||||
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
|
||||
self.layers = nn.ModuleList(
|
||||
[
|
||||
DeepseekDecoderLayer(config, layer_idx)
|
||||
for layer_idx in range(config.num_hidden_layers)
|
||||
]
|
||||
)
|
||||
self.norm = nn.RMSNorm(config.hidden_size, -1, config.rms_norm_eps, bias=False)
|
||||
|
||||
def forward(self, inputs: Tensor, paged_kv_cache: PagedKVCache):
|
||||
hidden_states = inputs
|
||||
for layer_id, layer in enumerate(self.layers):
|
||||
hidden_states = layer(hidden_states, paged_kv_cache, layer_id)
|
||||
hidden_states = self.norm(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class DeepseekForCausalLM(nn.Module):
|
||||
def __init__(self, config: DeepseekConfig):
|
||||
self.model = DeepseekModel(config)
|
||||
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
||||
self.vocab_size = config.vocab_size
|
||||
self.num_hidden_layers = config.num_hidden_layers
|
||||
self.hidden_size = config.hidden_size
|
||||
self.num_attention_heads = config.num_attention_heads
|
||||
self.num_key_value_heads = config.num_key_value_heads
|
||||
self.tensor_parallel_shards = config.tensor_parallel_shards
|
||||
self.head_dim = config.head_dim
|
||||
self.vocab_size = config.vocab_size
|
||||
self.rope_theta = config.rope_theta
|
||||
self.dtype = "float32"
|
||||
|
||||
def to(self, dtype: Optional[str] = None):
|
||||
super().to(dtype=dtype)
|
||||
if dtype is not None:
|
||||
self.dtype = dtype
|
||||
|
||||
def batch_forward(
|
||||
self,
|
||||
input_embeds: Tensor,
|
||||
paged_kv_cache: PagedKVCache,
|
||||
logit_positions: Optional[Tensor] = None,
|
||||
):
|
||||
op_ext.configure()
|
||||
|
||||
hidden_states = self.model(input_embeds, paged_kv_cache)
|
||||
if logit_positions is not None:
|
||||
hidden_states = op.take(hidden_states, logit_positions, axis=1)
|
||||
logits = self.lm_head(hidden_states)
|
||||
if logits.dtype != "float32":
|
||||
logits = logits.astype("float32")
|
||||
return logits
|
||||
|
||||
def embed(self, input_ids: Tensor):
|
||||
if self.tensor_parallel_shards > 1:
|
||||
input_ids = op.ccl_broadcast_from_worker0(input_ids)
|
||||
return self.model.embed_tokens(input_ids)
|
||||
|
||||
def prefill(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
|
||||
op_ext.configure()
|
||||
|
||||
hidden_states = self.model(input_embed, paged_kv_cache)
|
||||
hidden_states = index_last_token(hidden_states)
|
||||
|
||||
logits = self.lm_head(hidden_states)
|
||||
if logits.dtype != "float32":
|
||||
logits = logits.astype("float32")
|
||||
return logits, paged_kv_cache
|
||||
|
||||
def decode(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
|
||||
op_ext.configure()
|
||||
|
||||
hidden_states = self.model(input_embed, paged_kv_cache)
|
||||
logits = self.lm_head(hidden_states)
|
||||
if logits.dtype != "float32":
|
||||
logits = logits.astype("float32")
|
||||
return logits, paged_kv_cache
|
||||
|
||||
def batch_prefill(
|
||||
self,
|
||||
input_embeds: Tensor,
|
||||
logit_positions: Tensor,
|
||||
paged_kv_cache: PagedKVCache,
|
||||
):
|
||||
if self.tensor_parallel_shards > 1:
|
||||
logit_positions = op.ccl_broadcast_from_worker0(logit_positions)
|
||||
logits = self.batch_forward(input_embeds, paged_kv_cache, logit_positions)
|
||||
return logits, paged_kv_cache
|
||||
|
||||
def batch_decode(self, input_embeds: Tensor, paged_kv_cache: PagedKVCache):
|
||||
logits = self.batch_forward(input_embeds, paged_kv_cache)
|
||||
return logits, paged_kv_cache
|
||||
|
||||
def batch_verify(self, input_embeds: Tensor, paged_kv_cache: PagedKVCache):
|
||||
logits = self.batch_forward(input_embeds, paged_kv_cache)
|
||||
return logits, paged_kv_cache
|
||||
|
||||
def create_paged_kv_cache(
|
||||
self,
|
||||
max_batch_size: tirx.Var,
|
||||
max_total_seq_len: tirx.Var,
|
||||
prefill_chunk_size: tirx.Var,
|
||||
page_size: tirx.Var,
|
||||
support_sliding_window: tirx.Var,
|
||||
) -> PagedKVCache:
|
||||
return PagedKVCache.create_generic(
|
||||
attn_kind="mha",
|
||||
max_batch_size=max_batch_size,
|
||||
max_total_seq_len=max_total_seq_len,
|
||||
prefill_chunk_size=prefill_chunk_size,
|
||||
page_size=page_size,
|
||||
support_sliding_window=support_sliding_window,
|
||||
num_hidden_layers=self.num_hidden_layers,
|
||||
num_attention_heads=self.num_attention_heads // self.tensor_parallel_shards,
|
||||
num_key_value_heads=self.num_key_value_heads // self.tensor_parallel_shards,
|
||||
qk_head_dim=self.head_dim,
|
||||
v_head_dim=self.head_dim,
|
||||
rope_mode=RopeMode.NORMAL,
|
||||
rope_scale=1,
|
||||
rope_theta=self.rope_theta,
|
||||
dtype=self.dtype,
|
||||
)
|
||||
|
||||
def get_default_spec(self):
|
||||
mod_spec = {
|
||||
"embed": {
|
||||
"input_ids": nn.spec.Tensor(["seq_len"], "int32"),
|
||||
"$": {
|
||||
"param_mode": "packed",
|
||||
"effect_mode": "none",
|
||||
},
|
||||
},
|
||||
"prefill": {
|
||||
"input_embed": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
|
||||
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
||||
"$": {
|
||||
"param_mode": "packed",
|
||||
"effect_mode": "none",
|
||||
},
|
||||
},
|
||||
"decode": {
|
||||
"input_embed": nn.spec.Tensor([1, 1, self.hidden_size], self.dtype),
|
||||
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
||||
"$": {
|
||||
"param_mode": "packed",
|
||||
"effect_mode": "none",
|
||||
},
|
||||
},
|
||||
"batch_prefill": {
|
||||
"input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
|
||||
"logit_positions": nn.spec.Tensor(["batch_size"], "int32"),
|
||||
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
||||
"$": {
|
||||
"param_mode": "packed",
|
||||
"effect_mode": "none",
|
||||
},
|
||||
},
|
||||
"batch_decode": {
|
||||
"input_embeds": nn.spec.Tensor(["batch_size", 1, self.hidden_size], self.dtype),
|
||||
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
||||
"$": {
|
||||
"param_mode": "packed",
|
||||
"effect_mode": "none",
|
||||
},
|
||||
},
|
||||
"batch_verify": {
|
||||
"input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
|
||||
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
||||
"$": {
|
||||
"param_mode": "packed",
|
||||
"effect_mode": "none",
|
||||
},
|
||||
},
|
||||
"create_paged_kv_cache": {
|
||||
"max_batch_size": int,
|
||||
"max_total_seq_len": int,
|
||||
"prefill_chunk_size": int,
|
||||
"page_size": int,
|
||||
"support_sliding_window": int,
|
||||
"$": {
|
||||
"param_mode": "none",
|
||||
"effect_mode": "none",
|
||||
},
|
||||
},
|
||||
}
|
||||
return nn.spec.ModuleSpec.from_raw(mod_spec, self)
|
||||
Reference in New Issue
Block a user