717 lines
23 KiB
Python
717 lines
23 KiB
Python
# coding=utf-8
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import torch.distributed
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import numpy as np
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from torch import nn
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from transformers.activations import ACT2FN
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from transformers.configuration_utils import PretrainedConfig
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from typing import Optional, List, Tuple
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from text_generation_server.utils import paged_attention, flash_attn
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from text_generation_server.utils.flash_attn import (
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HAS_FLASH_ATTN_V2_ROCM,
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HAS_FLASH_ATTN_V2_CUDA,
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)
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from text_generation_server.utils.layers import (
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FastLinear,
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FastRMSNorm,
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TensorParallelRowLinear,
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TensorParallelColumnLinear,
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TensorParallelEmbedding,
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PositionRotaryEmbedding,
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TensorParallelHead,
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get_linear,
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)
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if not HAS_FLASH_ATTN_V2_CUDA and not HAS_FLASH_ATTN_V2_ROCM:
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raise ImportError("Mixtral model requires flash attn v2")
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try:
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import megablocks.ops as ops
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except ImportError:
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raise ImportError("Mixtral model requires megablocks to be installed")
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try:
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import stk
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except ImportError:
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raise ImportError("Mixtral model requires stk to be installed")
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class MixtralConfig(PretrainedConfig):
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model_type = "mixtral"
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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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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-05,
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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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pretraining_tp=1,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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sliding_window=4096,
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num_experts_per_tok=2,
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num_local_experts=8,
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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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# 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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self.num_key_value_heads = num_key_value_heads
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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.pretraining_tp = pretraining_tp
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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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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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 promote_scalar(x: torch.Tensor) -> torch.Tensor:
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return x.view(1) if len(x.size()) == 0 else x
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def load_attention(config, prefix, weights):
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if config.num_attention_heads != config.num_key_value_heads:
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return _load_gqa(config, prefix, weights)
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else:
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return TensorParallelColumnLinear.load_multi(
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config,
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prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
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dim=0,
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weights=weights,
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bias=False,
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)
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def _load_gqa(config, prefix: str, weights):
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assert config.hidden_size % config.num_attention_heads == 0
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assert config.num_attention_heads % weights.process_group.size() == 0
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weight = weights.get_multi_weights_col(
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prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
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quantize=config.quantize,
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dim=0,
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)
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if config.quantize not in ["gptq", "awq"]:
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weight = weight.to(dtype=weights.dtype).to(device=weights.device)
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head_size = config.hidden_size // config.num_attention_heads
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num_heads = config.num_attention_heads // weights.process_group.size()
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num_key_value_heads = config.num_key_value_heads // weights.process_group.size()
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assert list(weight.shape) == [
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(num_heads + 2 * num_key_value_heads) * head_size,
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config.hidden_size,
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], f"{list(weight.shape)} != {[(num_heads + 2 * config.num_key_value_heads) * head_size, config.hidden_size]}"
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return TensorParallelColumnLinear(
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get_linear(weight, bias=None, quantize=config.quantize)
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)
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def _load_experts(config, prefix, mat, weights):
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if config.quantize is not None:
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raise NotImplementedError("Mixtral does not support weight quantization yet.")
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assert mat in ["w1", "w2", "w3"]
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world_size = weights.process_group.size()
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rank = weights.process_group.rank()
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assert (
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config.intermediate_size % world_size == 0
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), f"The chosen size {config.intermediate_size} is not compatible with sharding on {world_size} shards"
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block_size = config.intermediate_size // world_size
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start = rank * block_size
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stop = (rank + 1) * block_size
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tensor = torch.empty(
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(config.num_local_experts * block_size, config.hidden_size),
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dtype=weights.dtype,
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device=weights.device,
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)
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for i in range(config.num_local_experts):
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slice_ = weights._get_slice(f"{prefix}.{i}.{mat}.weight")
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if mat == "w2":
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expert_slice = slice_[:, start:stop].t().contiguous()
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else:
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expert_slice = slice_[start:stop]
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tensor[i * block_size : (i + 1) * block_size] = expert_slice.to(
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dtype=weights.dtype
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).to(device=weights.device)
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return tensor
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class MixtralAttention(torch.nn.Module):
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def __init__(
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self,
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prefix: str,
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config,
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weights,
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):
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super().__init__()
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self.max_past = (
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config.sliding_window if config.sliding_window is not None else 0
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)
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self.num_heads = config.num_attention_heads
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self.hidden_size = config.hidden_size
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self.head_size = self.hidden_size // self.num_heads
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self.rotary_emb = PositionRotaryEmbedding.static(
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config=config,
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dim=self.head_size,
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base=config.rope_theta,
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device=weights.device,
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)
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self.softmax_scale = self.head_size**-0.5
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if self.num_heads % weights.process_group.size() != 0:
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raise ValueError(
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f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
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f"and `num_shards`: {weights.process_group.size()}"
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)
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self.num_heads = self.num_heads // weights.process_group.size()
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self.num_key_value_heads = (
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config.num_key_value_heads // weights.process_group.size()
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)
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self.query_key_value = load_attention(config, prefix, weights)
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self.o_proj = TensorParallelRowLinear.load(
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config,
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prefix=f"{prefix}.o_proj",
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weights=weights,
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bias=False,
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)
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self.num_groups = self.num_heads // self.num_key_value_heads
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self.kv_head_mapping = torch.arange(
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0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
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).repeat_interleave(self.num_groups)
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def forward(
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self,
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hidden_states,
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cos,
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sin,
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cu_seqlen_prefill,
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kv_cache,
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block_tables,
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slots,
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input_lengths,
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max_s,
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prefill_cache_indices,
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):
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qkv = self.query_key_value(hidden_states)
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query, kv = qkv.split(
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[
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self.head_size * self.num_heads,
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2 * self.head_size * self.num_key_value_heads,
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],
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dim=1,
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)
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query = query.view(-1, self.num_heads, self.head_size)
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kv = kv.view(-1, 2, self.num_key_value_heads, self.head_size)
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self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
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if prefill_cache_indices is not None:
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kv_to_cache = kv[prefill_cache_indices]
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else:
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kv_to_cache = kv
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paged_attention.reshape_and_cache(
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kv_to_cache[:, 0], kv_to_cache[:, 1], kv_cache[0], kv_cache[1], slots
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)
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# output tensor
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attn_output = torch.empty_like(query)
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# Prefill
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if cu_seqlen_prefill is not None:
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# flash attention
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flash_attn.attention(
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query,
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torch.select(kv, dim=1, index=0),
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torch.select(kv, dim=1, index=1),
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attn_output,
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cu_seqlen_prefill,
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max_s,
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self.softmax_scale,
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window_size_left=self.max_past,
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)
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# Decode
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else:
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paged_attention.attention(
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attn_output,
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query,
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kv_cache[0],
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kv_cache[1],
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self.kv_head_mapping,
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self.softmax_scale,
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block_tables,
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input_lengths,
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max_s,
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)
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return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
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@torch.jit.script
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def select_experts(gate_logits: torch.Tensor, top_k: int):
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# all_probs: (sequence_length, n_experts) and upcast for softmax
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all_probs = torch.nn.functional.softmax(gate_logits, dim=1, dtype=torch.float)
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# weights, selected_experts: (sequence_length, top-k)
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weights, selected_experts = torch.topk(all_probs, top_k, dim=-1)
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weights /= weights.sum(dim=-1, keepdim=True)
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weights = weights.view(-1)
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selected_experts = selected_experts.view(-1)
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return selected_experts, weights
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@torch.jit.script
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def round_up(x: torch.Tensor, value: int):
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return torch.div(x + (value - 1), value, rounding_mode="trunc") * value
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class BlockSparseMoE(nn.Module):
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"""
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Built on the paper and library Megablocks as described in
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https://arxiv.org/abs/2211.15841. This implementation is
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strictly equivalent to standard MoE with full capacity (no
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dropped tokens). It's faster since it formulates MoE operations
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in terms of block-sparse operations to accomodate imbalanced
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assignments of tokens to experts, whereas standard MoE either
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(1) drop tokens at the cost of reduced performance or (2) set
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capacity factor to number of experts and thus waste computation
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and memory on padding.
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"""
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def __init__(self, prefix, config: MixtralConfig, weights):
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super().__init__()
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self.hidden_dim = config.hidden_size
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self.ffn_dim = config.intermediate_size // weights.process_group.size()
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self.num_experts = config.num_local_experts
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self.top_k = config.num_experts_per_tok
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act = config.hidden_act
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if "gelu" in act:
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self.act = lambda x: torch.nn.functional.gelu(
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x,
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approximate="tanh"
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if act in ["gelu_fast", "gelu_pytorch_tanh"]
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else "none",
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)
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elif "silu" in act:
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self.act = torch.nn.functional.silu
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else:
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self.act = ACT2FN[act]
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# gating
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self.gate = FastLinear.load(config, f"{prefix}.gate", weights, bias=False)
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# merged expert weights, all of size (n_experts * ffn_dim, hidden_dim)
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self.w1 = _load_experts(config, f"{prefix}.experts", "w1", weights).t()
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self.w2 = _load_experts(config, f"{prefix}.experts", "w2", weights)
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self.w3 = _load_experts(config, f"{prefix}.experts", "w3", weights).t()
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self.offsets = None
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self.offsets_block_rows = 0
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self.process_group = weights.process_group
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# Calculate the number of bits needed to represent the expert indices
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# so that we can pass it to radix sort.
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self.sort_end_bit = max(int(np.ceil(np.log2(self.num_experts))), 1)
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self.blocking = 128
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self.quantize_scatter_num_bits = -1
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def topology(self, x: torch.Tensor, padded_bins: torch.Tensor):
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padded_tokens, _ = x.size()
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assert padded_tokens % self.blocking == 0
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assert self.ffn_dim % self.blocking == 0
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# Offsets for the sparse matrix. All rows have the
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# same number of nonzero blocks dictated by the
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# dimensionality of a single expert.
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block_rows = padded_tokens // self.blocking
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blocks_per_row = self.ffn_dim // self.blocking
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if self.offsets is None or block_rows > self.offsets_block_rows:
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self.offsets = torch.arange(
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0,
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block_rows * blocks_per_row + 1,
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blocks_per_row,
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dtype=torch.int32,
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device=x.device,
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)
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self.offsets_block_rows = block_rows
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offsets = self.offsets
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else:
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offsets = self.offsets[:block_rows]
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# Indices for the sparse matrix. The indices for
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# the intermediate matrix are dynamic depending
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# on the mapping of tokens to experts.
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column_indices = ops.topology(
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padded_bins, self.blocking, block_rows, blocks_per_row
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)
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# For now, use meta init to save the device memory.
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data = torch.empty(
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column_indices.numel(),
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self.blocking,
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self.blocking,
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dtype=x.dtype,
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device="meta",
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)
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shape = (padded_tokens, self.ffn_dim * self.num_experts)
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row_indices = stk.ops.row_indices(shape, data, offsets, column_indices)
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return stk.Matrix(
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shape,
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data,
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row_indices,
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column_indices,
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offsets,
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False,
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False,
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False,
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)
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def indices_and_padded_bins(self, selected_experts: torch.Tensor):
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# Sort the expert ids to produce the scatter/gather
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# indices for the permutation.
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# selected_experts = selected_experts.int()
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# returns bin_ids == num of experts for this sequence ? == unique selected experts?
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# and indices == how to sort tokens?
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bin_ids, indices = ops.sort(selected_experts, self.sort_end_bit)
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# bin_ids => [0, 0, 0, 2, 2, ...] => [num_tokens * top_k]
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# indices => [14, 32, 33, ...] => [num_tokens * top_k]
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# Histogram the expert ids to identify the number of
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# tokens routed to each expert.
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tokens_per_expert = ops.histogram(selected_experts, self.num_experts)
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# tokens_per_expert => [3, 0, 2, ...] => [num_experts]
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# Round the token counts up to the block size used in
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# the matrix muliplications. Caculate the starting
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# position of each bin.
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# List of size num_experts
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padded_tokens_per_expert = round_up(tokens_per_expert, self.blocking)
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# padded_tokens_per_expert => [128, O, 128, ...]
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# Cumulative selected experts per token
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padded_bins = ops.inclusive_cumsum(padded_tokens_per_expert, 0)
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padded_bins = promote_scalar(padded_bins)
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# padded_bins => [128, 128, 256, ...]
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# Calculate the bin bounds for the sorted tokens.
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bins = ops.inclusive_cumsum(tokens_per_expert, 0)
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bins = promote_scalar(bins)
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# bins => [3, 3, 5, ...]
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return indices, bin_ids, bins, padded_bins, tokens_per_expert
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@torch.inference_mode()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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x: (sequence_length, model_dim)
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gate_logits: (sequence_length, n_experts)
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"""
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# optional reshape
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input_shape = x.shape
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x = x.view(-1, input_shape[-1])
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# gate_logits: (sequence_length, n_experts)
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gate_logits = self.gate(x)
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selected_experts, weights = select_experts(gate_logits, self.top_k)
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(
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indices,
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bin_ids,
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bins,
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padded_bins,
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_,
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) = self.indices_and_padded_bins(selected_experts)
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# Permute tokens and pad to prepare expert computation
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# (top_k * sequence_length + padding, model_dim)
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x = ops.padded_gather(x, indices, bin_ids, bins, padded_bins, self.top_k)
|
|
|
|
# Create the sparse matrix topology
|
|
with torch.no_grad():
|
|
topo = self.topology(x, padded_bins)
|
|
|
|
# Perform the expert computation
|
|
# First Dense x Dense -> Sparse for w1 and w3,
|
|
# (top_k * sequence_length + padding, ffn_dim * n_experts)
|
|
x = stk.Matrix(
|
|
topo.size(),
|
|
self.act(stk.ops.sdd(x, self.w1, topo).data)
|
|
* stk.ops.sdd(x, self.w3, topo).data,
|
|
topo.row_indices,
|
|
topo.column_indices,
|
|
topo.offsets,
|
|
topo.column_indices_t,
|
|
topo.offsets_t,
|
|
topo.block_offsets_t,
|
|
)
|
|
|
|
# Then Sparse x Dense -> Dense for w2
|
|
# (top_k * sequence_length + padding, model_dim)
|
|
x = stk.ops.dsd(x, self.w2)
|
|
|
|
# Permute back and remove padding
|
|
# (sequence_length, model_dim)
|
|
x = ops.padded_scatter(
|
|
x,
|
|
indices,
|
|
bin_ids,
|
|
weights,
|
|
bins,
|
|
padded_bins,
|
|
self.top_k,
|
|
self.quantize_scatter_num_bits,
|
|
).view(*input_shape)
|
|
|
|
if self.process_group.size() > 1:
|
|
torch.distributed.all_reduce(x, group=self.process_group)
|
|
|
|
return x.view(*input_shape)
|
|
|
|
|
|
class MixtralLayer(nn.Module):
|
|
def __init__(self, layer_id, config, weights):
|
|
super().__init__()
|
|
prefix = f"model.layers.{layer_id}"
|
|
|
|
self.self_attn = MixtralAttention(
|
|
prefix=f"{prefix}.self_attn", config=config, weights=weights
|
|
)
|
|
self.block_sparse_moe = BlockSparseMoE(
|
|
f"{prefix}.block_sparse_moe", config, weights
|
|
)
|
|
|
|
self.input_layernorm = FastRMSNorm.load(
|
|
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
|
|
)
|
|
self.post_attention_layernorm = FastRMSNorm.load(
|
|
prefix=f"{prefix}.post_attention_layernorm",
|
|
weights=weights,
|
|
eps=config.rms_norm_eps,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
residual,
|
|
cos,
|
|
sin,
|
|
cu_seqlen_prefill,
|
|
kv_cache,
|
|
block_tables,
|
|
slots,
|
|
input_lengths,
|
|
max_s,
|
|
prefill_cache_indices,
|
|
):
|
|
normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
|
|
|
|
# Self Attention
|
|
attn_output = self.self_attn(
|
|
normed_hidden_states,
|
|
cos,
|
|
sin,
|
|
cu_seqlen_prefill,
|
|
kv_cache,
|
|
block_tables,
|
|
slots,
|
|
input_lengths,
|
|
max_s,
|
|
prefill_cache_indices,
|
|
)
|
|
|
|
# faster post attention rms norm
|
|
normed_attn_res_output, attn_res = self.post_attention_layernorm(
|
|
attn_output, res
|
|
)
|
|
|
|
block_sparse_moe_output = self.block_sparse_moe(normed_attn_res_output)
|
|
|
|
return block_sparse_moe_output, attn_res
|
|
|
|
|
|
class MixtralModel(torch.nn.Module):
|
|
def __init__(self, config, weights):
|
|
super().__init__()
|
|
|
|
self.embed_tokens = TensorParallelEmbedding(
|
|
prefix="model.embed_tokens", weights=weights
|
|
)
|
|
|
|
self.layers = nn.ModuleList(
|
|
[
|
|
MixtralLayer(
|
|
layer_id,
|
|
config,
|
|
weights,
|
|
)
|
|
for layer_id in range(config.num_hidden_layers)
|
|
]
|
|
)
|
|
self.norm = FastRMSNorm.load(
|
|
prefix="model.norm", weights=weights, eps=config.rms_norm_eps
|
|
)
|
|
|
|
self.head_size = self.layers[0].self_attn.head_size
|
|
self.num_heads = self.layers[0].self_attn.num_heads
|
|
self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
position_ids: torch.Tensor,
|
|
cu_seqlen_prefill: Optional[torch.Tensor],
|
|
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
|
|
block_tables: torch.Tensor,
|
|
slots: torch.Tensor,
|
|
input_lengths: torch.Tensor,
|
|
max_s: int,
|
|
prefill_cache_indices: Optional[torch.Tensor],
|
|
) -> torch.Tensor:
|
|
hidden_states = self.embed_tokens(input_ids)
|
|
|
|
# Get rotary cos and sin for this forward
|
|
# Avoid to index in each layer
|
|
cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
|
|
position_ids, max_s, hidden_states.dtype
|
|
)
|
|
|
|
residual = None
|
|
for i, layer in enumerate(self.layers):
|
|
hidden_states, residual = layer(
|
|
hidden_states,
|
|
residual,
|
|
cos,
|
|
sin,
|
|
cu_seqlen_prefill,
|
|
kv_cache[i],
|
|
block_tables,
|
|
slots,
|
|
input_lengths,
|
|
max_s,
|
|
prefill_cache_indices,
|
|
)
|
|
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class FlashMixtralForCausalLM(torch.nn.Module):
|
|
def __init__(self, config, weights):
|
|
super().__init__()
|
|
|
|
self.model = MixtralModel(config, weights)
|
|
self.lm_head = TensorParallelHead.load(
|
|
config,
|
|
prefix="lm_head",
|
|
weights=weights,
|
|
)
|
|
self.max_past = config.sliding_window
|
|
if self.max_past is None:
|
|
raise ValueError("max_past cannot be None")
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
position_ids: torch.Tensor,
|
|
cu_seqlen_prefill: Optional[torch.Tensor],
|
|
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
|
|
block_tables: torch.Tensor,
|
|
slots: torch.Tensor,
|
|
input_lengths: torch.Tensor,
|
|
max_s: int,
|
|
prefill_cache_indices: Optional[torch.Tensor],
|
|
lm_head_indices: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
if prefill_cache_indices is not None:
|
|
# Slots also need to be sliced as it has the same size as the whole kv tensor
|
|
slots = slots[prefill_cache_indices]
|
|
else:
|
|
# Clamp in decode mode as paged attention requires clamped values whereas the flash attention
|
|
# kernel requires the true values
|
|
max_s = min(self.max_past, max_s)
|
|
input_lengths = torch.clamp(input_lengths, max=self.max_past)
|
|
|
|
hidden_states = self.model(
|
|
input_ids,
|
|
position_ids,
|
|
cu_seqlen_prefill,
|
|
kv_cache,
|
|
block_tables,
|
|
slots,
|
|
input_lengths,
|
|
max_s,
|
|
prefill_cache_indices,
|
|
)
|
|
if lm_head_indices is not None:
|
|
hidden_states = hidden_states[lm_head_indices]
|
|
logits = self.lm_head(hidden_states)
|
|
return logits
|