560 lines
19 KiB
Python
560 lines
19 KiB
Python
from typing import Iterable, Optional, Tuple, Union
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import torch
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from torch import nn
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from transformers.configuration_utils import PretrainedConfig
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from sglang.srt.distributed import get_tensor_model_parallel_world_size
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from sglang.srt.layers.dp_attention import get_attention_tp_rank, get_attention_tp_size
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from sglang.srt.layers.linear import (
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QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear,
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)
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from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
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from sglang.srt.layers.moe.topk import TopK
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from sglang.srt.layers.pooler import Pooler, PoolingType
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.layers.rotary_embedding import get_rope
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from sglang.srt.layers.utils import PPMissingLayer
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from sglang.srt.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE,
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.model_loader.weight_utils import (
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default_weight_loader,
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maybe_remap_kv_scale_name,
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)
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from sglang.srt.utils import add_prefix, make_layers
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class PhiMoEConfig(PretrainedConfig):
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model_type = "phimoe"
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def __init__(
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self,
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vocab_size=32000,
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hidden_size=4096,
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intermediate_size=14336,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=8,
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head_dim=None,
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hidden_act="silu",
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max_position_embeddings=4096 * 32,
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initializer_range=0.02,
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rms_norm_eps=1e-5,
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use_cache=True,
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pad_token_id=None,
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bos_token_id=1,
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eos_token_id=2,
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tie_word_embeddings=False,
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rope_theta=1e6,
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sliding_window=None,
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attention_dropout=0.0,
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num_experts_per_tok=2,
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num_local_experts=16,
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output_router_logits=False,
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router_aux_loss_coef=0.001,
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router_jitter_noise=0.0,
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attention_bias=False,
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lm_head_bias=False,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.sliding_window = sliding_window
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self.attention_bias = attention_bias
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self.lm_head_bias = lm_head_bias
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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if head_dim is None:
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head_dim = hidden_size // num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.head_dim = head_dim
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.attention_dropout = attention_dropout
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self.num_experts_per_tok = num_experts_per_tok
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self.num_local_experts = num_local_experts
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self.output_router_logits = output_router_logits
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self.router_aux_loss_coef = router_aux_loss_coef
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self.router_jitter_noise = router_jitter_noise
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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def sparsemixer(scores, jitter_eps=0.01):
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################ Select first expert (topk=2) ################
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# compute mask for sparsity
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mask_logits_threshold, max_ind = scores.max(dim=-1, keepdim=True)
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factor = scores.abs().clamp(min=mask_logits_threshold)
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mask_logits_threshold = ((mask_logits_threshold - scores) / factor) > (
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2 * jitter_eps
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)
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# apply mask
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masked_gates = scores.masked_fill(mask_logits_threshold, float("-inf"))
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selected_experts = max_ind
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# compute scores for gradients
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masked_gates = torch.softmax(masked_gates, dim=-1)
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multiplier_o = masked_gates.gather(dim=-1, index=selected_experts)
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multiplier = multiplier_o
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# masked out first expert
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masked_scores = torch.scatter(
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scores,
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-1,
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selected_experts,
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float("-inf"),
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)
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################ Select second expert (topk=2) ################
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# compute mask for sparsity
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mask_logits_threshold, max_ind = masked_scores.max(dim=-1, keepdim=True)
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factor = scores.abs().clamp(min=mask_logits_threshold)
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mask_logits_threshold = ((mask_logits_threshold - scores) / factor) > (
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2 * jitter_eps
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)
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# apply mask
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masked_gates_top2 = masked_scores.masked_fill(mask_logits_threshold, float("-inf"))
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selected_experts_top2 = max_ind
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# compute scores for gradients
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masked_gates_top2 = torch.softmax(masked_gates_top2, dim=-1)
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multiplier_top2 = masked_gates_top2.gather(dim=-1, index=selected_experts_top2)
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multiplier = torch.concat((multiplier, multiplier_top2), dim=-1)
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selected_experts = torch.concat((selected_experts, selected_experts_top2), dim=-1)
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return (
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multiplier,
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selected_experts,
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)
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def phimoe_routing_function(
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hidden_states: torch.Tensor,
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gating_output: torch.Tensor,
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topk: int,
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renormalize: bool,
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):
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assert hidden_states.shape[0] == gating_output.shape[0], "Number of tokens mismatch"
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assert topk == 2, "Only top-2 routing is supported"
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assert renormalize is False, "Renormalization is not supported"
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topk_weights, topk_ids = sparsemixer(gating_output)
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return topk_weights, topk_ids
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class PhiMoE(nn.Module):
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"""A tensor-parallel MoE implementation for PhiMoE that shards each expert
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across all ranks.
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Each expert's weights are sharded across all ranks and a fused MoE
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kernel is used for the forward pass, and finally we reduce the outputs
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across ranks.
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"""
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def __init__(
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self,
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num_experts: int,
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top_k: int,
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hidden_size: int,
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intermediate_size: int,
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layer_id: int,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.hidden_size = hidden_size
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self.tp_size = get_tensor_model_parallel_world_size()
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# Gate always runs at half / full precision for now.
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self.gate = ReplicatedLinear(
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hidden_size,
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num_experts,
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bias=False,
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quant_config=None,
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)
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self.topk = TopK(
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top_k=top_k,
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renormalize=False,
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custom_routing_function=phimoe_routing_function,
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)
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self.experts = FusedMoE(
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num_experts=num_experts,
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top_k=top_k,
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hidden_size=hidden_size,
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intermediate_size=intermediate_size,
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reduce_results=True,
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quant_config=quant_config,
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prefix=add_prefix("experts", prefix),
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)
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def forward(
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self, hidden_states: torch.Tensor, forward_batch: Optional[ForwardBatch] = None
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) -> torch.Tensor:
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# NOTE: hidden_states can have either 1D or 2D shape.
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orig_shape = hidden_states.shape
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hidden_states = hidden_states.view(-1, self.hidden_size)
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router_logits, _ = self.gate(hidden_states)
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topk_output = self.topk(hidden_states, router_logits)
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final_hidden_states = self.experts(hidden_states, topk_output)
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return final_hidden_states.view(orig_shape)
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class PhiMoEAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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head_dim: Optional[int] = None,
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max_position: int = 4096 * 32,
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rope_theta: float = 10000,
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layer_id: int = 0,
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attention_bias: bool = False,
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quant_config: Optional[QuantizationConfig] = None,
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rope_scaling: Optional[dict] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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attn_tp_rank = get_attention_tp_rank()
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attn_tp_size = get_attention_tp_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % attn_tp_size == 0
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self.num_heads = self.total_num_heads // attn_tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= attn_tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % attn_tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert attn_tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // attn_tp_size)
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if head_dim is None:
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head_dim = hidden_size // num_heads
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self.head_dim = head_dim
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=attention_bias,
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quant_config=quant_config,
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tp_rank=attn_tp_rank,
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tp_size=attn_tp_size,
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prefix=add_prefix("qkv_proj", prefix),
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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hidden_size,
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bias=attention_bias,
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quant_config=quant_config,
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tp_rank=attn_tp_rank,
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tp_size=attn_tp_size,
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prefix=add_prefix("o_proj", prefix),
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)
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position,
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base=int(self.rope_theta),
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rope_scaling=self.rope_scaling,
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)
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self.attn = RadixAttention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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layer_id=layer_id,
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quant_config=quant_config,
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prefix=add_prefix("attn", prefix),
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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forward_batch: ForwardBatch,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v, forward_batch)
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output, _ = self.o_proj(attn_output)
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return output
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class PhiMoEDecoderLayer(nn.Module):
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def __init__(
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self,
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config: PhiMoEConfig,
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layer_id: int,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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rope_theta = getattr(config, "rope_theta", 10000)
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self.self_attn = PhiMoEAttention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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max_position=config.max_position_embeddings,
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num_kv_heads=config.num_key_value_heads,
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head_dim=getattr(
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config, "head_dim", self.hidden_size // config.num_attention_heads
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),
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rope_theta=rope_theta,
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layer_id=layer_id,
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attention_bias=config.attention_bias,
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quant_config=quant_config,
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rope_scaling=config.rope_scaling,
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prefix=add_prefix("self_attn", prefix),
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)
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self.block_sparse_moe = PhiMoE(
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num_experts=config.num_local_experts,
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top_k=config.num_experts_per_tok,
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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layer_id=layer_id,
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quant_config=quant_config,
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prefix=add_prefix("block_sparse_moe", prefix),
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)
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self.input_layernorm = nn.LayerNorm(
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config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True
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)
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self.post_attention_layernorm = nn.LayerNorm(
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config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: Optional[torch.Tensor],
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forward_batch: ForwardBatch,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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forward_batch=forward_batch,
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)
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hidden_states = hidden_states + residual
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.block_sparse_moe(
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hidden_states, forward_batch=forward_batch
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)
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hidden_states = hidden_states + residual
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return hidden_states, residual
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class PhiMoEModel(nn.Module):
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def __init__(
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self,
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config: PhiMoEConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.config = config
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self.quant_config = quant_config
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self.vocab_size = config.vocab_size
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=add_prefix("embed_tokens", prefix),
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)
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self.layers = make_layers(
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config.num_hidden_layers,
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lambda idx, prefix: PhiMoEDecoderLayer(
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config, int(prefix.split(".")[-1]), quant_config, prefix=prefix
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),
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prefix=add_prefix("layers", prefix),
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)
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self.norm = nn.LayerNorm(
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config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True
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)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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input_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor]:
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if input_embeds is None:
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hidden_states = self.embed_tokens(input_ids)
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else:
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hidden_states = input_embeds
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residual = None
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for layer in self.layers:
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hidden_states, residual = layer(
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positions, hidden_states, residual, forward_batch=forward_batch
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)
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hidden_states = self.norm(hidden_states)
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return hidden_states
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class PhiMoEForCausalLM(nn.Module):
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def __init__(
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self,
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config: PhiMoEConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.config = config
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self.quant_config = quant_config
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self.model = PhiMoEModel(
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config=config, quant_config=quant_config, prefix=add_prefix("model", prefix)
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)
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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padding_size=DEFAULT_VOCAB_PADDING_SIZE,
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quant_config=quant_config,
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bias=True,
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prefix=add_prefix("lm_head", prefix),
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)
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if self.config.tie_word_embeddings:
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self.lm_head.weight = self.model.embed_tokens.weight
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self.logits_processor = LogitsProcessor(config)
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self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
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@torch.no_grad()
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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inputs_embeds: Optional[torch.Tensor] = None,
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get_embedding: bool = False,
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) -> LogitsProcessorOutput:
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hidden_states = self.model(input_ids, positions, forward_batch, inputs_embeds)
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if not get_embedding:
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return self.logits_processor(
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input_ids, hidden_states, self.lm_head, forward_batch
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)
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|
|
|
else:
|
|
return self.pooler(hidden_states, forward_batch)
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
]
|
|
|
|
expert_params_mapping = FusedMoE.make_expert_params_mapping(
|
|
ckpt_gate_proj_name="w1",
|
|
ckpt_down_proj_name="w2",
|
|
ckpt_up_proj_name="w3",
|
|
num_experts=self.config.num_local_experts,
|
|
)
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
for name, loaded_weight in weights:
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
for mapping in expert_params_mapping:
|
|
param_name, weight_name, expert_id, shard_id = mapping
|
|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(
|
|
param,
|
|
loaded_weight,
|
|
name,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id,
|
|
)
|
|
break
|
|
else:
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
# Remapping the name of FP8 kv-scale.
|
|
name = maybe_remap_kv_scale_name(name, params_dict)
|
|
if name is None:
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = getattr(
|
|
param, "weight_loader", default_weight_loader
|
|
)
|
|
weight_loader(param, loaded_weight)
|
|
|
|
|
|
EntryClass = PhiMoEForCausalLM
|