wehub-resource-sync
523 lines
21 KiB
Python
523 lines
21 KiB
Python
"""
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Implementation for Nemotron architecture.
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"""
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import dataclasses
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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.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 NemotronConfig(ConfigBase):
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"""Configuration of the Nemotron model."""
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vocab_size: int
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max_position_embeddings: int
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hidden_size: int
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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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rope_theta: int = 10000
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partial_rotary_factor: float = 0.5
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rope_scaling: Optional[Dict[str, Any]] = None # noqa: UP006
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norm_eps: float = 1e-5
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head_dim: int = 0
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tie_word_embeddings: bool = False
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mlp_bias: bool = False
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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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pipeline_parallel_stages: int = 1
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max_batch_size: int = 1
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disaggregation: bool = False
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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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self.context_window_size = self.max_position_embeddings
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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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self.rotary_dim = int(self.partial_rotary_factor * self.head_dim)
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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 NemotronMLP(nn.Module):
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"""Nemotron MLP module."""
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def __init__(self, config: NemotronConfig):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.mlp_bias)
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self.down_proj = nn.Linear(
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config.intermediate_size, config.hidden_size, bias=config.mlp_bias
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)
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def forward(self, x: Tensor) -> Tensor:
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"""Forward pass of the MLP module."""
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out = self.up_proj(x)
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out = op.square(op.relu(out))
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out = self.down_proj(out)
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return out
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class NemotronEmbedding(nn.Embedding):
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"""The embedding module that can be shared with the final head. From Qwen2Embedding."""
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def lm_head_forward(self, x: Tensor):
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"""The lm_head forwarding, which transposes the weight and multiplies
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with the input tensor.
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"""
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weight = nn.op.permute_dims(self.weight)
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return nn.op.matmul(x, weight, out_dtype="float32")
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class NemotronLayerNorm1P(nn.LayerNorm):
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"""Nemotron LayerNorm1P module."""
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def __init__(self, normalized_shape: int, eps: float = 1e-5, elementwise_affine: bool = True):
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super().__init__(normalized_shape, eps, elementwise_affine)
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def forward(self, x: Tensor) -> Tensor:
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"""Forward pass of the tweaked LayerNorm module."""
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return op.layer_norm(
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x,
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normalized_shape=self.normalized_shape,
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weight=self.weight + 1,
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bias=self.bias,
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eps=self.eps,
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)
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class NemotronAttention(nn.Module):
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def __init__(self, config: NemotronConfig):
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self.head_dim = config.head_dim
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self.num_q_heads = config.num_attention_heads // config.tensor_parallel_shards
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assert config.num_key_value_heads % config.tensor_parallel_shards == 0, (
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f"num_kv_heads({config.num_key_value_heads}) must be divisible by tensor_parallel_shards" # noqa: E501
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)
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assert config.num_key_value_heads >= config.tensor_parallel_shards, (
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f"Too large tensor_parallel_shards, must be smaller than {config.num_key_value_heads}"
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)
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self.num_kv_heads = config.num_key_value_heads // config.tensor_parallel_shards
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self.qkv_proj = nn.Linear(
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in_features=config.hidden_size,
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out_features=(self.num_q_heads + 2 * self.num_kv_heads) * self.head_dim,
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bias=False,
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)
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self.o_proj = nn.Linear(self.num_q_heads * self.head_dim, config.hidden_size, bias=False)
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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_q_heads, self.num_kv_heads
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b, s, _ = hidden_states.shape
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# QKV Projection
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qkv = self.qkv_proj(hidden_states)
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qkv = op.reshape(qkv, (b, s, h_q + h_kv + h_kv, d))
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# Attention
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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_q_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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return self.o_proj(output)
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class NemotronDecoderLayer(nn.Module):
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def __init__(self, config: NemotronConfig):
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self.self_attn = NemotronAttention(config)
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self.mlp = NemotronMLP(config)
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self.input_layernorm = NemotronLayerNorm1P(config.hidden_size, config.norm_eps)
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self.post_attention_layernorm = NemotronLayerNorm1P(config.hidden_size, config.norm_eps)
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def _set_tp():
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def _set(layer, hint):
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layer.weight.attrs["shard_strategy"] = hint
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hd = config.head_dim
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q = self.self_attn.num_q_heads * hd
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k = self.self_attn.num_kv_heads * hd
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v = self.self_attn.num_kv_heads * hd
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_set(
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self.self_attn.qkv_proj,
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tp.ShardSingleDim("_shard_qkv", segs=[q, k, v], dim=0),
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)
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_set(self.self_attn.o_proj, tp.ShardSingleDim("_shard_o", dim=1))
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_set(self.mlp.up_proj, tp.ShardSingleDim("_shard_mlp_up", dim=1))
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_set(self.mlp.down_proj, tp.ShardSingleDim("_shard_mlp_down", dim=1))
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self.tensor_parallel_shards = config.tensor_parallel_shards
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_set_tp()
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def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
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out = self.self_attn(self.input_layernorm(hidden_states), paged_kv_cache, layer_id)
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hidden_states = self._apply_residual(out, residual=hidden_states)
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out = self.mlp(self.post_attention_layernorm(hidden_states))
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hidden_states = self._apply_residual(out, residual=hidden_states)
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return hidden_states
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def _apply_residual(self, out, residual):
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if self.tensor_parallel_shards > 1:
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return op.ccl_allreduce(out, "sum") + residual
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return out + residual
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class NemotronModel(nn.Module):
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def __init__(self, config: NemotronConfig):
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assert config.hidden_size % config.num_attention_heads == 0
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self.embed_tokens = NemotronEmbedding("vocab_size", config.hidden_size)
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self.layers = nn.ModuleList(
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[NemotronDecoderLayer(config) for _ in range(config.num_hidden_layers)]
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)
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self.norm = NemotronLayerNorm1P(config.hidden_size, config.norm_eps)
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self.num_layers_per_stage = (
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config.num_hidden_layers + config.pipeline_parallel_stages - 1
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) // config.pipeline_parallel_stages
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# Compute pipeline layer partition.
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layers_per_stage = (
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config.num_hidden_layers + config.pipeline_parallel_stages - 1
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) // config.pipeline_parallel_stages
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self.layer_partition = [
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i * layers_per_stage for i in range(config.pipeline_parallel_stages)
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] + [config.num_hidden_layers]
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def forward(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
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hidden_states = input_embed
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for layer_id, layer in enumerate(self.layers):
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if layer_id != 0 and layer_id in self.layer_partition:
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hidden_states = op_ext.pipeline_stage_boundary(hidden_states)
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hidden_states = layer(hidden_states, paged_kv_cache, layer_id)
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hidden_states = self.norm(hidden_states)
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return hidden_states
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class NemotronForCausalLM(nn.Module):
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def __init__(self, config: NemotronConfig):
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self.model = NemotronModel(config)
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self.tie_word_embeddings = config.tie_word_embeddings
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if not config.tie_word_embeddings:
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self.lm_head = nn.Linear(config.hidden_size, "vocab_size", bias=False)
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self.num_hidden_layers = config.num_hidden_layers
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self.num_attention_heads = config.num_attention_heads
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self.num_key_value_heads = config.num_key_value_heads
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self.head_dim = config.head_dim
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self.hidden_size = config.hidden_size
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self.vocab_size = config.vocab_size
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self.rope_scaling = config.rope_scaling
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self.rope_theta = config.rope_theta
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self.rotary_dim = config.rotary_dim
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self.tensor_parallel_shards = config.tensor_parallel_shards
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self.disaggregation = config.disaggregation
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self.dtype = "float32"
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def _set_pp():
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# hidden layers
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for layer_id in range(config.num_hidden_layers):
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stage = layer_id // (config.num_hidden_layers // config.pipeline_parallel_stages)
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for _, param in self.model.layers[layer_id].named_parameters():
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param.attrs["pipeline_stages"] = [stage]
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# last stage
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last_stage = config.pipeline_parallel_stages - 1
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self.model.norm.weight.attrs["pipeline_stages"] = [last_stage]
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# embedding table and lm_head is required by all stages
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all_stages = list(range(config.pipeline_parallel_stages))
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self.model.embed_tokens.weight.attrs["pipeline_stages"] = all_stages
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if not config.tie_word_embeddings:
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self.lm_head.weight.attrs["pipeline_stages"] = all_stages
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_set_pp()
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def to(self, dtype: Optional[str] = None):
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super().to(dtype=dtype)
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if dtype is not None:
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self.dtype = dtype
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def batch_forward(
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self,
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input_embeds: Tensor,
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paged_kv_cache: PagedKVCache,
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logit_positions: Optional[Tensor] = None,
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):
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op_ext.configure()
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hidden_states = self.model(input_embeds, paged_kv_cache)
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if logit_positions is not None:
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if self.tensor_parallel_shards > 1:
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logit_positions = op.ccl_broadcast_from_worker0(logit_positions)
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hidden_states = op.take(hidden_states, logit_positions, axis=1)
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return self.get_logits(hidden_states)
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def batch_forward_to_last_hidden_states(
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self,
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input_embeds: Tensor,
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paged_kv_cache: PagedKVCache,
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):
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op_ext.configure()
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hidden_states = self.model(input_embeds, paged_kv_cache)
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return hidden_states
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def embed(self, input_ids: Tensor):
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if self.tensor_parallel_shards > 1:
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input_ids = op.ccl_broadcast_from_worker0(input_ids)
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return self.model.embed_tokens(input_ids)
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def get_logits(self, hidden_states: Tensor):
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op_ext.configure()
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if self.tie_word_embeddings:
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logits = self.model.embed_tokens.lm_head_forward(hidden_states)
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else:
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logits = self.lm_head(hidden_states)
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if logits.dtype != "float32":
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logits = logits.astype("float32")
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return logits
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def batch_select_last_hidden_states(self, hidden_states: Tensor, logit_positions: Tensor):
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op_ext.configure()
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if self.tensor_parallel_shards > 1:
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logit_positions = op.ccl_broadcast_from_worker0(logit_positions)
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hidden_states = op.take(hidden_states, logit_positions, axis=0)
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return hidden_states
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def prefill(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
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op_ext.configure()
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hidden_states = self.model(input_embed, paged_kv_cache)
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hidden_states = index_last_token(hidden_states)
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logits = self.get_logits(hidden_states)
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return logits, paged_kv_cache
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def decode(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
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op_ext.configure()
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hidden_states = self.model(input_embed, paged_kv_cache)
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logits = self.get_logits(hidden_states)
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return logits, paged_kv_cache
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def prefill_to_last_hidden_states(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
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op_ext.configure()
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hidden_states = self.model(input_embed, paged_kv_cache)
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return hidden_states, paged_kv_cache
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def decode_to_last_hidden_states(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
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op_ext.configure()
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hidden_states = self.model(input_embed, paged_kv_cache)
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return hidden_states, paged_kv_cache
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def batch_prefill(
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self,
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input_embeds: Tensor,
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logit_positions: Tensor,
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paged_kv_cache: PagedKVCache,
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):
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logits = self.batch_forward(input_embeds, paged_kv_cache, logit_positions)
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return logits, paged_kv_cache
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def batch_decode(self, input_embeds: Tensor, paged_kv_cache: PagedKVCache):
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logits = self.batch_forward(input_embeds, paged_kv_cache)
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return logits, paged_kv_cache
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def batch_verify(self, input_embeds: Tensor, paged_kv_cache: PagedKVCache):
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logits = self.batch_forward(input_embeds, paged_kv_cache)
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return logits, paged_kv_cache
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def batch_prefill_to_last_hidden_states(
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self, input_embeds: Tensor, paged_kv_cache: PagedKVCache
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):
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hidden_states = self.batch_forward_to_last_hidden_states(input_embeds, paged_kv_cache)
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return hidden_states, paged_kv_cache
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def batch_decode_to_last_hidden_states(
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self, input_embeds: Tensor, paged_kv_cache: PagedKVCache
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):
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hidden_states = self.batch_forward_to_last_hidden_states(input_embeds, paged_kv_cache)
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return hidden_states, paged_kv_cache
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def batch_verify_to_last_hidden_states(
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self, input_embeds: Tensor, paged_kv_cache: PagedKVCache
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):
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hidden_states = self.batch_forward_to_last_hidden_states(input_embeds, paged_kv_cache)
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return hidden_states, paged_kv_cache
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def create_paged_kv_cache(
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self,
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max_batch_size: tirx.Var,
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max_total_seq_len: tirx.Var,
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prefill_chunk_size: tirx.Var,
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page_size: tirx.Var,
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support_sliding_window: tirx.Var,
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) -> PagedKVCache:
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return PagedKVCache.create_generic(
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attn_kind="mha",
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max_batch_size=max_batch_size,
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max_total_seq_len=max_total_seq_len,
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prefill_chunk_size=prefill_chunk_size,
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page_size=page_size,
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support_sliding_window=support_sliding_window,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads // self.tensor_parallel_shards,
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num_key_value_heads=self.num_key_value_heads // self.tensor_parallel_shards,
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qk_head_dim=self.head_dim,
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v_head_dim=self.head_dim,
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rope_mode=RopeMode.NORMAL,
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rope_scale=1,
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rope_theta=self.rope_theta,
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rope_scaling=self.rope_scaling,
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rotary_dim=self.rotary_dim,
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layer_partition=self.model.layer_partition,
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enable_disaggregation=self.disaggregation,
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dtype=self.dtype,
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)
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def get_default_spec(self):
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mod_spec = {
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"embed": {
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"input_ids": nn.spec.Tensor(["seq_len"], "int32"),
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"$": {
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"param_mode": "packed",
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"effect_mode": "none",
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},
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},
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"get_logits": {
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"hidden_states": nn.spec.Tensor(["seq_len", self.hidden_size], self.dtype),
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"$": {
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"param_mode": "packed",
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"effect_mode": "none",
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},
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},
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"batch_select_last_hidden_states": {
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"hidden_states": nn.spec.Tensor(["seq_len", self.hidden_size], self.dtype),
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"logit_positions": nn.spec.Tensor(["batch_size"], "int32"),
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"$": {
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"param_mode": "none",
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"effect_mode": "none",
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},
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},
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"prefill": {
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"input_embed": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
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"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
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"$": {
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"param_mode": "packed",
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"effect_mode": "none",
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},
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},
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"decode": {
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"input_embed": nn.spec.Tensor([1, 1, self.hidden_size], self.dtype),
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|
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
|
"$": {
|
|
"param_mode": "packed",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"prefill_to_last_hidden_states": {
|
|
"input_embed": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
|
|
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
|
"$": {
|
|
"param_mode": "packed",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"decode_to_last_hidden_states": {
|
|
"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",
|
|
},
|
|
},
|
|
"batch_prefill_to_last_hidden_states": {
|
|
"input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
|
|
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
|
"$": {
|
|
"param_mode": "packed",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"batch_decode_to_last_hidden_states": {
|
|
"input_embeds": nn.spec.Tensor(["batch_size", 1, self.hidden_size], self.dtype),
|
|
"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
|
|
"$": {
|
|
"param_mode": "packed",
|
|
"effect_mode": "none",
|
|
},
|
|
},
|
|
"batch_verify_to_last_hidden_states": {
|
|
"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)
|