【马斯克开源GROK-1模型】意味着什么?_from jax.experimental.shard_map import shard_map-程序员宅基地

技术标签: JAX  深度学习框架  机器学习  深度学习  人工智能  马斯克  杂谈  开源  

目录

1.激动人心的消息 

Grok-1 根据 Apache License 2.0 开放源代码

        题外话-介绍JAX框架

2.grok-1模型参数介绍

3.推理grok-1模型需要多大显存?

4.grok-1开源意味着什么? 

5.最后,让我们一同期待开源grok-1模型的训练代码!


 

        "我们正在发布我们的大型语言模型 Grok-1 的基础模型权重和网络架构",xAI在一篇博客文章中宣布。“Grok-1”是一个由xAI从头开始训练的 3140 亿参数专家混合(Mixture-of-Experts)模型。        

1.激动人心的消息 

Grok-1 根据 Apache License 2.0 开放源代码

        Grok-1 根据 Apache License 2.0 开放源代码,允许商业使用、修改和分发,但不能注册商标,用户也不承担任何责任或保证。此外,用户必须复制原始许可证和版权声明,并说明所做的修改。

        目前,Grok-1 的代码和模型权重已经上线GitHub,收获37K+start。

github连接如下:grok-1

        别太激动,xAI 强调,由于模型规模较大(314B参数),需要有足够GPU内存的机器才能使用示例代码测试模型。并且开源的模型是基于JAX框架去写的,并不是常用的pytorch、TensorFlow等框架。根据xAI官网消息,Grok-1是3140亿参数的混合专家模型,是迄今为止参数量最大的开源大语言模型,是Llama2开源大模型的4倍。并且尽管Grok-1已经开源。但是也些关键信息并没有透露。首先,xAI宣称他们是基于一套高效的基础设施进行训练的。这套高效的基础设施架构并未透露

        题外话-介绍JAX框架

        JAX 是一个专为加速器优化的数组计算和程序转换设计的 Python 库,主要目标是高性能数值计算和大规模机器学习。它通过改进的 Autograd[12] 版本,能够自动计算 Python 和 NumPy 函数的导数,支持复杂结构的自动微分,包括循环、分支、递归和闭包,并能进行多阶导数计算

2.grok-1模型参数介绍

        官网给出的grok-1模型参数简介

  • Parameters: 314B
  • Architecture: Mixture of 8 Experts (MoE)
  • Experts Utilization: 2 experts used per token
  • Layers: 64
  • Attention Heads: 48 for queries, 8 for keys/values
  • Embedding Size: 6,144
  • Tokenization: SentencePiece tokenizer with 131,072 tokens
  • Additional Features:
    • Rotary embeddings (RoPE)
    • Supports activation sharding and 8-bit quantization
  • Maximum Sequence Length (context): 8,192 tokens

        公开的model.py文件如下所示:

# Copyright 2024 X.AI Corp.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import functools
import logging
import re
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, NamedTuple, Optional, Sequence, Tuple, Union

import haiku as hk
import jax
import jax.experimental.maps
import jax.numpy as jnp
from jax import config, tree_util
from jax.experimental.shard_map import shard_map
from jax.lax import with_sharding_constraint as pjit_sharding_constraint
from jax.sharding import PartitionSpec
from jax.sharding import PartitionSpec as P

config.update("jax_spmd_mode", "allow_all")

logger = logging.getLogger(__name__)
rank_logger = logging.getLogger("rank")


@dataclass
class QuantizedWeight8bit:
    weight: jnp.array
    scales: jnp.array

    @property
    def shape(self):
        return self.weight.shape


tree_util.register_pytree_node(
    QuantizedWeight8bit,
    lambda qw: ([qw.weight, qw.scales], ()),
    lambda _, children: QuantizedWeight8bit(children[0], children[1]),
)


class TrainingState(NamedTuple):
    """Container for the training state."""

    params: hk.Params


def _match(qs, ks):
    """Return True if regexes in qs match any window of strings in tuple ks."""
    # compile regexes and force complete match
    qts = tuple(map(lambda x: re.compile(x + "$"), qs))
    for i in range(len(ks) - len(qs) + 1):
        matches = [x.match(y) for x, y in zip(qts, ks[i:])]
        if matches and all(matches):
            return True
    return False


def with_sharding_constraint(x, constraint):
    if jax.experimental.maps.thread_resources.env.physical_mesh.empty:
        return x
    else:
        return pjit_sharding_constraint(x, constraint)


def cast_bfloat16(x):
    if x.dtype.kind == "f":
        return x.astype(jnp.bfloat16)
    else:
        return x


def ffn_size(emb_size, widening_factor):
    _ffn_size = int(widening_factor * emb_size) * 2 // 3
    _ffn_size = _ffn_size + (8 - _ffn_size) % 8  # ensure it's a multiple of 8
    logger.debug(f"emd_size: {emb_size} adjusted ffn_size: {_ffn_size}")
    return _ffn_size


def apply_rules(rules):
    def _apply_rules(path, value):
        del value  # Unused.

        path_list = [str(i.key).split("/") for i in path if isinstance(i, jax.tree_util.DictKey)]
        flattened_path = jax.tree_util.tree_flatten(path_list)[0]

        for rule, replacement in rules:
            if _match(rule, flattened_path):
                if isinstance(replacement, PartitionSpec):
                    if "layer_stack" in flattened_path:
                        replacement = PartitionSpec(None, *replacement)
                rank_logger.debug(f"Apply {replacement} to {flattened_path} with rule {rule}")
                return replacement
        rank_logger.info(f"{flattened_path} no matching found!")
        return None

    return _apply_rules


TRANSFORMER_PARTITION_RULES = [
    # attention
    (("multi_head_attention", "(query|key|value)", "w"), P("data", "model")),
    (("multi_head_attention", "(query|key|value)", "b"), P(None)),
    (("multi_head_attention", "linear", "w"), P("model", "data")),
    (("multi_head_attention", "linear", "b"), P(None)),
    # mlp
    ((r"decoder_layer_[0-9]+", "linear", "w"), P("data", "model")),
    ((r"decoder_layer_[0-9]+", "linear", "b"), P(None)),
    ((r"decoder_layer_[0-9]+", "linear_v", "w"), P("data", "model")),
    ((r"decoder_layer_[0-9]+", "linear_v", "b"), P(None)),
    (
        (r"decoder_layer_[0-9]+", "linear_1", "w"),
        P(
            "model",
            "data",
        ),
    ),
    ((r"decoder_layer_[0-9]+", "linear_1", "b"), P(None)),
    # layer norms
    ((r"decoder_layer_[0-9]+", "layer_norm", "offset"), P(None)),
    ((r"decoder_layer_[0-9]+", "layer_norm", "scale"), P(None)),
    ((r"decoder_layer_[0-9]+", "layer_norm_1", "offset"), P(None)),
    ((r"decoder_layer_[0-9]+", "layer_norm_1", "scale"), P(None)),
    # rms norms
    ((r"decoder_layer_[0-9]+", "rms_norm", "scale"), P(None)),
    ((r"decoder_layer_[0-9]+", "rms_norm_1", "scale"), P(None)),
    ((r"decoder_layer_[0-9]+", "rms_norm_2", "scale"), P(None)),
    ((r"decoder_layer_[0-9]+", "rms_norm_3", "scale"), P(None)),
    # router
    (("router", "w"), P("data")),
    # moe mlp
    (("moe", "linear", "w"), P(None, "data", "model")),
    (("moe", "linear", "b"), P(None)),
    (("moe", "linear_v", "w"), P(None, "data", "model")),
    (("moe", "linear_v", "b"), P(None)),
    (("moe", "linear_1", "w"), P(None, "model", "data")),
    (("moe", "linear_1", "b"), P(None)),
    # layer norms
    (("moe", "layer_norm", "offset"), P(None)),
    (("moe", "layer_norm", "scale"), P(None)),
    (("moe", "layer_norm_1", "offset"), P(None)),
    (("moe", "layer_norm_1", "scale"), P(None)),
    # rms norms
    (("moe", "rms_norm", "scale"), P(None)),
    (("moe", "rms_norm_1", "scale"), P(None)),
    (("moe", "rms_norm_2", "scale"), P(None)),
    (("moe", "rms_norm_3", "scale"), P(None)),
]

LM_PARTITION_RULES = [
    # Embedding layer.
    (
        ("language_model", "positional_embeddings"),
        P(None, ("data", "model")),
    ),
    (
        ("language_model", "in_out_embed", "embeddings"),
        P(None, ("data", "model")),
    ),
    # Final RMSNorm.
    (("language_model", "rms_norm"), P(None)),
]
TOP_K = 8


class KVMemory(NamedTuple):
    k: Optional[jax.Array]
    v: Optional[jax.Array]
    step: Optional[jax.Array]


def init_layer_memories(
    batch_size: int,
    sequence_len: int,
    num_kv_heads: int,
    key_size: int,
    num_layers: int,
    step: Optional[jax.Array] = None,
    dtype=jnp.bfloat16,
):
    return [
        KVMemory(
            k=jnp.zeros((batch_size, sequence_len, num_kv_heads, key_size), dtype=dtype),
            v=jnp.zeros((batch_size, sequence_len, num_kv_heads, key_size), dtype=dtype),
            step=step,
        )
        for _ in range(num_layers)
    ]


class Memory(NamedTuple):
    # Self-attention key/value cache.
    layers: List[KVMemory]


class Router(hk.Module):
    def __init__(
        self,
        num_selected_experts: int,
        data_axis: Union[str, Tuple[str, ...]] = "data",
        model_axis: Union[str, Tuple[str, ...]] = "model",
        shard_activations: bool = False,
        mesh: Any = None,
        name: str = "router",
    ):
        super().__init__(name)
        self.shard_activations = shard_activations
        self.data_axis = data_axis
        self.model_axis = model_axis
        self.mesh = mesh
        self.num_selected_experts = num_selected_experts

    def compute_routing_prob(
        self, inputs: jax.Array, padding_mask: Optional[jax.Array], num_experts: int
    ):
        return self._compute_routing_prob(inputs, padding_mask, num_experts)

    @hk.transparent
    def _compute_routing_prob(
        self,
        inputs: jax.Array,
        padding_mask: Optional[jax.Array],
        num_experts: int,
    ):
        # Using fp32 for the routing prob computation.
        inputs = jax.lax.convert_element_type(inputs, jnp.float32)

        # [batch_size, seq_len, num_experts]
        routing_logits = self._router_weights(inputs, num_experts, sharding=P("data"))
        assert routing_logits.dtype == jnp.float32
        routing_probs = jax.nn.softmax(routing_logits)

        if padding_mask is not None:
            routing_probs *= padding_mask

        return routing_probs, routing_logits, 0

    @hk.transparent
    def _router_weights(
        self,
        x: jax.Array,
        num_experts: int,
        sharding: Optional[P] = None,
    ):
        fprop_dtype = x.dtype
        if not x.shape:
            raise ValueError("Input must not be scalar.")

        input_size = self.input_size = x.shape[-1]
        w = hk.get_parameter(
            "w", [input_size, num_experts], jnp.float32, init=hk.initializers.Constant(0)
        )
        if sharding:
            w = with_sharding_constraint(w, sharding)

        out = jnp.dot(x, w.astype(fprop_dtype))
        return out


class MoELayer(hk.Module):
    def __init__(
        self,
        num_experts: int,
        layer_fn: Callable,
        router: Router,
        mesh: Any = None,
        shard_activations: bool = False,
        data_axis: Union[str, Tuple[str, ...]] = "data",
        model_axis: Union[str, Tuple[str, ...]] = "model",
        name: Optional[str] = "moe",
    ):
        super().__init__(name)
        self.num_experts = num_experts
        self.layer_fn = layer_fn
        self.router = router
        self.mesh = mesh
        self.shard_activations = shard_activations
        self.data_axis = data_axis
        self.model_axis = model_axis

    @hk.transparent
    def _inference_call(self, inputs: jax.Array, padding_mask: Optional[jax.Array] = None):
        routing_probs, _, _ = self.router.compute_routing_prob(
            inputs, padding_mask, self.num_experts
        )
        expert_gate, expert_index = jax.lax.top_k(routing_probs, k=self.router.num_selected_experts)
        tmp = jnp.reshape(inputs, (inputs.shape[0] * inputs.shape[1], inputs.shape[2]))
        broad_inputs = jnp.tile(tmp[:, jnp.newaxis, :], (1, self.router.num_selected_experts, 1))
        broad_inputs = jnp.reshape(
            broad_inputs, (broad_inputs.shape[0] * broad_inputs.shape[1], broad_inputs.shape[2])
        )
        init_fn, _ = hk.transform(self.layer_fn)
        vmapped_init_fn = jax.vmap(init_fn, in_axes=0, out_axes=0)
        lifted_init_fn = hk.experimental.transparent_lift(vmapped_init_fn)
        # Fetch the vmapped params of the DenseBlock.
        params = lifted_init_fn(
            jax.random.split(jax.random.PRNGKey(1), self.num_experts),
            jnp.zeros((self.num_experts, 1, 1, inputs.shape[-1])),
        )

        # Index and prob are in the shape [m, 2] indicating which token assigned to which experts.
        # b: num_expert
        # m: token or sequence dim
        # k: input embed dim
        # n: output embed dim
        # e: the number of experts chosen for each token
        @functools.partial(
            shard_map,
            mesh=self.mesh,
            in_specs=(
                P(self.data_axis, None),
                P(None, None, self.model_axis),
                P(None, None, self.model_axis),
                P(None),
                P(None),
            ),
            out_specs=P(self.data_axis, self.model_axis),
            check_rep=False,
        )
        def moe_slow_matmul1(input, weight, scales, index, prob):
            weight = weight * scales
            one_hot_indices = jax.nn.one_hot(index.reshape(-1), 8, axis=0)
            all_expert_output = jnp.einsum("mk,bkn->bmn", input, weight)
            output = jnp.einsum("bm,bmn->mn", one_hot_indices, all_expert_output)
            return output

        @functools.partial(
            shard_map,
            mesh=self.mesh,
            in_specs=(
                P(self.data_axis, self.model_axis),
                P(None, self.model_axis, None),
                P(None, self.model_axis, None),
                P(None),
                P(None),
            ),
            out_specs=P(self.data_axis, None),
            check_rep=False,
        )
        def moe_slow_matmul2(input, weight, scales, index, prob):
            weight = weight * scales
            one_hot_indices = jax.nn.one_hot(index.reshape(-1), 8, axis=0)
            all_expert_output = jnp.einsum("mk,bkn->bmn", input, weight)
            output = jnp.einsum("bm,bmn->mn", one_hot_indices, all_expert_output)
            return jax.lax.psum(output, axis_name="model")

        if hasattr(params["linear"]["w"], "scales"):
            x = moe_slow_matmul1(
                broad_inputs,
                params["linear_v"]["w"].weight,
                params["linear_v"]["w"].scales,
                expert_index,
                expert_gate,
            )
            y = moe_slow_matmul1(
                broad_inputs,
                params["linear"]["w"].weight,
                params["linear"]["w"].scales,
                expert_index,
                expert_gate,
            )
            y = jax.nn.gelu(y)
            out = moe_slow_matmul2(
                x * y,
                params["linear_1"]["w"].weight,
                params["linear_1"]["w"].scales,
                expert_index,
                expert_gate,
            )
            out = jnp.reshape(
                out,
                [
                    inputs.shape[0],
                    inputs.shape[1],
                    self.router.num_selected_experts,
                    out.shape[-1],
                ],
            )
            out = expert_gate[:, :, :, None].astype(jnp.bfloat16) * out
            out = jnp.sum(out, axis=2)
            out = out.astype(jnp.bfloat16)
        else:
            # This is only here so that we can construct a valid init_fn with this code.
            return inputs
        return out

    def __call__(self, inputs: jax.Array, padding_mask: jax.Array):
        return self._inference_call(inputs)


class MHAOutput(NamedTuple):
    """Outputs of the multi-head attention operation."""

    embeddings: jax.Array
    memory: Any


class DecoderOutput(NamedTuple):
    embeddings: jax.Array
    memory: Any


class TransformerOutput(NamedTuple):
    embeddings: jax.Array
    memory: Any


@dataclass
class TransformerConfig:
    emb_size: int
    key_size: int
    num_q_heads: int
    num_kv_heads: int
    num_layers: int
    vocab_size: int = 128 * 1024
    widening_factor: float = 4.0

    attn_output_multiplier: float = 1.0

    name: Optional[str] = None

    num_experts: int = -1
    capacity_factor: float = 1.0
    num_selected_experts: int = 1

    init_scale: float = 1.0
    shard_activations: bool = False

    # Used for activation sharding.
    data_axis: Union[str, Tuple[str, ...]] = "data"
    model_axis: Union[str, Tuple[str, ...]] = "model"

    def __post_init__(self):
        if isinstance(self.data_axis, list):
            self.data_axis = tuple(self.data_axis)
        if isinstance(self.model_axis, list):
            self.model_axis = tuple(self.model_axis)

    def partition_rules(self):
        return TRANSFORMER_PARTITION_RULES

    def make(self, mesh=None) -> "Transformer":
        data_axis = tuple(self.data_axis) if isinstance(self.data_axis, list) else self.data_axis
        model_axis = (
            tuple(self.model_axis) if isinstance(self.model_axis, list) else self.model_axis
        )

        return Transformer(
            num_q_heads=self.num_q_heads,
            num_kv_heads=self.num_kv_heads,
            widening_factor=self.widening_factor,
            key_size=self.key_size,
            init_scale=self.init_scale,
            mesh=mesh,
            attn_output_multiplier=self.attn_output_multiplier,
            shard_activations=self.shard_activations,
            num_layers=self.num_layers,
            num_experts=self.num_experts,
            num_selected_experts=self.num_selected_experts,
            data_axis=data_axis,
            model_axis=model_axis,
        )

    def get_memory_sharding(self):
        return Memory(
            layers=[
                KVMemory(
                    k=P(self.data_axis, self.model_axis),
                    v=P(self.data_axis, self.model_axis),
                    step=P(self.data_axis),
                )
                for _ in range(self.num_layers)
            ],
        )


def hk_rms_norm(
    x: jax.Array,
    fixed_scale=False,
    sharding=P(None),
) -> jax.Array:
    """Applies a unique LayerNorm to x with default settings."""
    ln = RMSNorm(axis=-1, create_scale=not fixed_scale, sharding=sharding)
    return ln(x)


def make_attention_mask(
    query_input: jax.Array,
    key_input: jax.Array,
    pairwise_fn: Callable[..., Any] = jnp.multiply,
    dtype: Any = jnp.bfloat16,
):
    """Mask-making helper for attention weights.

    In case of 1d inputs (i.e., `[batch..., len_q]`, `[batch..., len_kv]`, the
    attention weights will be `[batch..., heads, len_q, len_kv]` and this
    function will produce `[batch..., 1, len_q, len_kv]`.

    Args:
      query_input: a batched, flat input of query_length size
      key_input: a batched, flat input of key_length size
      pairwise_fn: broadcasting elementwise comparison function
      dtype: mask return dtype

    Returns:
      A `[batch..., 1, len_q, len_kv]` shaped mask for 1d attention.
    """
    mask = pairwise_fn(jnp.expand_dims(query_input, axis=-1), jnp.expand_dims(key_input, axis=-2))
    mask = jnp.expand_dims(mask, axis=-3)
    return mask.astype(dtype)


class Linear(hk.Linear):
    def __init__(
        self,
        output_size: int,
        with_bias: bool = True,
        sharding: Optional[P] = None,
        mesh: Any = None,
        name: Optional[str] = None,
        shard_axis: int = 0,
    ):
        super().__init__(
            output_size=output_size,
            with_bias=with_bias,
            name=name,
        )
        self.sharding = sharding
        self.mesh = mesh
        self.shard_axis = shard_axis

    def __call__(
        self,
        inputs: jax.Array,
    ) -> jax.Array:
        """Computes a linear transform of the input."""

        fprop_dtype = inputs.dtype
        if not inputs.shape:
            raise ValueError("Input must not be scalar.")

        input_size = self.input_size = inputs.shape[-1]
        output_size = self.output_size

        w = hk.get_parameter(
            "w", [input_size, output_size], jnp.float32, init=hk.initializers.Constant(0)
        )

        if hasattr(w, "scales"):
            shape = inputs.shape
            inputs = jnp.reshape(inputs, (-1, shape[-1]))

            @functools.partial(
                shard_map,
                mesh=self.mesh,
                in_specs=(self.sharding, self.sharding),
                out_specs=self.sharding,
                check_rep=False,
            )
            def mul(w, s):
                return w.astype(s.dtype) * s

            w = mul(w.weight, w.scales)
        out = jnp.dot(inputs, w.astype(fprop_dtype))
        if self.with_bias:
            b = hk.get_parameter(
                "b", [self.output_size], jnp.float32, init=hk.initializers.Constant(0)
            )
            b = jnp.broadcast_to(b, out.shape)
            out = out + b.astype(fprop_dtype)

        return out


class RMSNorm(hk.RMSNorm):

    def __init__(
        self,
        axis: Union[int, Sequence[int], slice],
        eps: float = 1e-5,
        name: Optional[str] = None,
        create_scale: bool = True,
        sharding: Optional[P] = None,
    ):
        super().__init__(axis, eps, create_scale=create_scale, name=name)
        self.sharding = sharding

    def __call__(self, inputs: jax.Array):
        fprop_dtype = inputs.dtype
        param_shape = (inputs.shape[-1],)
        if self.create_scale:
            scale = hk.get_parameter(
                "scale",
                param_shape,
                dtype=jnp.float32,
                init=hk.initializers.Constant(0),
            )
            if self.sharding:
                scale = with_sharding_constraint(scale, self.sharding)
            scale = jnp.broadcast_to(scale.astype(jnp.float32), inputs.shape)
        else:
            scale = 1.0
        inputs = inputs.astype(jnp.float32)
        scale = scale.astype(jnp.float32)
        mean_squared = jnp.mean(jnp.square(inputs), axis=[-1], keepdims=True)
        mean_squared = jnp.broadcast_to(mean_squared, inputs.shape)

        normed_inputs = inputs * jax.lax.rsqrt(mean_squared + self.eps)

        outputs = scale * normed_inputs

        return outputs.astype(fprop_dtype)


def rotate_half(
    x: jax.Array,
) -> jax.Array:
    """Obtain the rotated counterpart of each feature"""
    x1, x2 = jnp.split(x, 2, axis=-1)
    return jnp.concatenate((-x2, x1), axis=-1)


class RotaryEmbedding(hk.Module):
    """Applies rotary embeddings (RoPE) to the input sequence tensor,
    as described in https://arxiv.org/abs/2104.09864.

    Attributes:
        dim (int): Dimensionality of the feature vectors
        base_exponent (int): Base exponent to compute embeddings from
    """

    def __init__(
        self,
        dim: int,
        name: Optional[str] = None,
        base_exponent: int = 10000,
    ):
        super().__init__(name)
        self.dim = dim
        self.base_exponent = base_exponent
        assert self.dim % 2 == 0

    def __call__(
        self,
        x: jax.Array,
        seq_dim: int,
        offset: jax.Array,
        const_position: Optional[int] = None,
        t: Optional[jax.Array] = None,
    ) -> jax.Array:
        fprop_dtype = x.dtype
        # Compute the per-dimension frequencies
        exponents = jnp.arange(0, self.dim, 2, dtype=jnp.float32)
        inv_freq = jnp.asarray(
            1.0 / (self.base_exponent ** (exponents / self.dim)), dtype=jnp.float32
        )

        if jnp.shape(offset) == ():
            # Offset can be a scalar or one offset per batch element.
            offset = jnp.expand_dims(offset, 0)

        # Compute the per element phase (to pass into sin and cos)
        if const_position:
            t = const_position * jnp.ones(
                (
                    1,
                    x.shape[seq_dim],
                ),
                dtype=jnp.float32,
            )
        elif t is None:
            t = jnp.arange(x.shape[seq_dim], dtype=jnp.float32) + jnp.expand_dims(offset, -1)
        phase = jnp.einsum("bi,j->bij", t, inv_freq)
        phase = jnp.tile(phase, reps=(1, 2))[:, :, None, :]

        x = x * jnp.cos(phase) + rotate_half(x) * jnp.sin(phase)
        x = x.astype(fprop_dtype)

        return x


class MultiHeadAttention(hk.Module):
    def __init__(
        self,
        num_q_heads: int,
        num_kv_heads: int,
        key_size: int,
        *,
        with_bias: bool = True,
        value_size: Optional[int] = None,
        model_size: Optional[int] = None,
        attn_output_multiplier: 1.0,
        data_axis: Union[str, Tuple[str, ...]] = "data",
        model_axis: Union[str, Tuple[str, ...]] = "model",
        name: Optional[str] = None,
    ):
        super().__init__(name=name)
        self.num_q_heads = num_q_heads
        self.num_kv_heads = num_kv_heads
        self.key_size = key_size
        self.value_size = value_size or key_size
        self.model_size = model_size or key_size * num_q_heads
        self.data_axis = data_axis
        self.model_axis = model_axis
        self.attn_output_multiplier = attn_output_multiplier
        self.with_bias = with_bias

    def __call__(
        self,
        query: jax.Array,
        key: Optional[jax.Array],
        value: Optional[jax.Array],
        mask: Optional[jax.Array] = None,
        kv_memory: Optional[KVMemory] = None,
        mesh: Any = None,
    ) -> MHAOutput:
        # In shape hints below, we suppress the leading dims [...] for brevity.
        # Hence e.g. [A, B] should be read in every case as [..., A, B].
        sequence_length = query.shape[1]
        projection = self._linear_projection
        use_memory = False
        if kv_memory is not None:
            if kv_memory.k is None:
                assert kv_memory.v is None
                assert key is not None
                assert value is not None
            else:
                assert kv_memory.v is not None
                use_memory = True
        else:
            assert key is not None
            assert value is not None

        # Check that the keys and values have consistent batch size and sequence length.
        if not use_memory:
            assert key.shape[:2] == value.shape[:2], f"key/value shape: {key.shape}/{value.shape}"

        if mask is not None:
            assert mask.ndim == 4
            assert mask.shape[0] in {
                1,
                query.shape[0],
            }, f"mask/query shape: {mask.shape}/{query.shape}"
            if not use_memory:
                assert key.shape[0] in {
                    1,
                    query.shape[0],
                }, f"key/query shape: {key.shape}/{query.shape}"
            assert mask.shape[1] == 1
            assert mask.shape[2] in {
                1,
                query.shape[1],
            }, f"mask/query shape: {mask.shape}/{query.shape}"
            if not use_memory:
                assert mask.shape[3] in {
                    1,
                    key.shape[1],
                }, f"mask/query shape: {mask.shape}/{key.shape}"

        # Compute key/query/values (overload K/Q/V to denote the respective sizes).
        assert self.num_q_heads % self.num_kv_heads == 0
        query_heads = projection(
            query,
            self.key_size,
            self.num_q_heads,
            name="query",
            sharding=P("data", "model"),
            mesh=mesh,
        )  # [B, T', H, Q=K]

        new_memory = None
        key_heads = projection(
            key,
            self.key_size,
            self.num_kv_heads,
            name="key",
            sharding=P("data", "model"),
            mesh=mesh,
        )  # [B, T, H, K]
        value_heads = projection(
            value,
            self.value_size,
            self.num_kv_heads,
            name="value",
            sharding=P("data", "model"),
            mesh=mesh,
        )  # [B, T, H, V]

        rotate = RotaryEmbedding(dim=self.key_size, base_exponent=int(1e4))
        key_heads = rotate(key_heads, seq_dim=1, offset=(kv_memory.step if kv_memory else 0))
        query_heads = rotate(query_heads, seq_dim=1, offset=(kv_memory.step if kv_memory else 0))

        @functools.partial(jax.vmap)
        def update_into(mem, start, update):
            return jax.lax.dynamic_update_slice_in_dim(mem, update, start, axis=0)

        if kv_memory:
            if mesh is not None:

                @functools.partial(
                    shard_map,
                    mesh=mesh,
                    in_specs=(
                        P("data", None, "model"),
                        P("data"),
                        P("data", None, "model"),
                    ),
                    out_specs=P("data", None, "model"),
                    check_rep=False,
                )
                def update_into_shmap(mems, starts, updates):
                    return update_into(mems, starts, updates)

                key_heads = update_into_shmap(kv_memory.k, kv_memory.step, key_heads)
                value_heads = update_into_shmap(kv_memory.v, kv_memory.step, value_heads)
            else:
                key_heads = update_into(kv_memory.k, kv_memory.step, key_heads)
                value_heads = update_into(kv_memory.v, kv_memory.step, value_heads)

            new_step = kv_memory.step + sequence_length
            memory_mask = jnp.arange(kv_memory.k.shape[1]) < new_step[:, None]
            memory_mask = memory_mask[:, None, None, :]  # [B, H, T, T]
            if mask is not None:
                mask = memory_mask * mask
            else:
                mask = memory_mask

            new_memory = KVMemory(
                k=key_heads,
                v=value_heads,
                step=new_step,
            )
        # Add separate dimension for grouped query heads.
        query_heads = with_sharding_constraint(query_heads, P(self.data_axis, None, "model", None))
        key_heads = with_sharding_constraint(key_heads, P(self.data_axis, None, "model", None))
        value_heads = with_sharding_constraint(value_heads, P(self.data_axis, None, "model", None))
        b, t, h, d = query_heads.shape
        _, _, kv_h, _ = key_heads.shape
        assert h % kv_h == 0, f"query_heads {h} must be a multiple of kv_heads {kv_h}"

        query_heads = jnp.reshape(query_heads, (b, t, kv_h, h // kv_h, d))
        query_heads = with_sharding_constraint(
            query_heads, P(self.data_axis, None, "model", None, None)
        )

        # Compute attention weights.
        # Attention softmax is always carried out in fp32.
        attn_logits = jnp.einsum("...thHd,...Thd->...hHtT", query_heads, key_heads).astype(
            jnp.float32
        )
        attn_logits *= self.attn_output_multiplier
        max_attn_val = jnp.array(30.0, dtype=attn_logits.dtype)
        attn_logits = max_attn_val * jnp.tanh(attn_logits / max_attn_val)

        mask = mask[:, :, None, :, :]

        if mask is not None:
            if mask.ndim != attn_logits.ndim:
                raise ValueError(
                    f"Mask dimensionality {mask.ndim} must match logits dimensionality "
                    f"{attn_logits.ndim} for {mask.shape}/{attn_logits.shape}."
                )
            attn_logits = jnp.where(mask, attn_logits, -1e30)
        attn_weights = jax.nn.softmax(attn_logits).astype(query.dtype)  # [H, T', T]

        # Weight the values by the attention and flatten the head vectors.
        attn = jnp.einsum("...hHtT,...Thd->...thHd", attn_weights, value_heads)
        attn = with_sharding_constraint(attn, P(self.data_axis, None, "model", None, None))
        leading_dims = attn.shape[:2]
        attn = jnp.reshape(attn, (*leading_dims, -1))  # [T', H*V]
        attn = with_sharding_constraint(attn, P(self.data_axis, None, "model"))
        # Apply another projection to get the final embeddings.
        final_projection = Linear(
            self.model_size,
            with_bias=False,
            sharding=P("model", "data"),
            mesh=mesh,
        )
        return MHAOutput(final_projection(attn), new_memory)

    @hk.transparent
    def _linear_projection(
        self,
        x: jax.Array,
        head_size: int,
        num_heads: int,
        sharding: Optional[P] = None,
        name: Optional[str] = None,
        mesh: Any = None,
    ) -> jax.Array:
        y = Linear(
            num_heads * head_size,
            with_bias=False,
            name=name,
            sharding=sharding,
            mesh=mesh,
        )(x)
        *leading_dims, _ = x.shape
        return y.reshape((*leading_dims, num_heads, head_size))


@dataclass
class MHABlock(hk.Module):
    """A MHA Block"""

    num_q_heads: int
    num_kv_heads: int
    key_size: int
    attn_output_multiplier: float = 1.0
    mesh: Any = None
    data_axis: Union[str, Tuple[str, ...]] = "data"
    model_axis: Union[str, Tuple[str, ...]] = "model"

    @hk.transparent
    def __call__(
        self,
        inputs: jax.Array,  # [B, T, D]
        mask: jax.Array,  # [B, 1, T, T] or [B, 1, 1, T] or B[1, 1, 1, 1]
        layer_memory: Optional[KVMemory],
    ) -> MHAOutput:
        _, _, model_size = inputs.shape
        assert mask.ndim == 4, f"shape: {mask.shape}"
        assert mask.shape[2] in {1, inputs.shape[1]}, str(mask.shape)
        assert mask.shape[3] in {1, inputs.shape[1]}, str(mask.shape)
        side_input = inputs

        def attn_block(query, key, value, mask, memory) -> MHAOutput:
            return MultiHeadAttention(
                num_q_heads=self.num_q_heads,
                num_kv_heads=self.num_kv_heads,
                key_size=self.key_size,
                model_size=model_size,
                data_axis=self.data_axis,
                model_axis=self.model_axis,
                attn_output_multiplier=self.attn_output_multiplier,
            )(
                query,
                key,
                value,
                mask,
                memory,
                mesh=self.mesh,
            )

        attn_output = attn_block(inputs, side_input, side_input, mask, layer_memory)
        h_attn = attn_output.embeddings

        return attn_output._replace(embeddings=h_attn)


@dataclass
class DenseBlock(hk.Module):
    num_q_heads: int
    num_kv_heads: int
    key_size: int
    widening_factor: float = 4.0
    sharding_constraint: bool = False
    mesh: Any = None

    @hk.transparent
    def __call__(
        self,
        inputs: jax.Array,  # [B, T, D]
    ) -> jax.Array:  # [B, T, D]
        _, _, model_size = inputs.shape
        h_v = Linear(
            ffn_size(
                model_size,
                self.widening_factor,
            ),
            with_bias=False,
            mesh=self.mesh,
            sharding=P("data", "model"),
            name="linear_v",
        )(inputs)
        h_w1 = jax.nn.gelu(
            Linear(
                ffn_size(
                    model_size,
                    self.widening_factor,
                ),
                with_bias=False,
                mesh=self.mesh,
                sharding=P("data", "model"),
            )(inputs)
        )
        h_dense = Linear(
            model_size,
            with_bias=False,
            sharding=P("model", "data"),
            mesh=self.mesh,
            shard_axis=1,
        )(h_w1 * h_v)

        return h_dense


@dataclass
class DecoderLayer(hk.Module):
    """A transformer stack."""

    num_q_heads: int
    num_kv_heads: int
    key_size: int
    num_layers: int
    # MoE.
    num_experts: int
    layer_index: Optional[int] = None
    num_selected_experts: int = 1
    widening_factor: float = 4.0
    name: Optional[str] = None
    data_axis: Union[str, Tuple[str, ...]] = "data"
    model_axis: Union[str, Tuple[str, ...]] = "model"
    shard_activations: bool = False
    attn_output_multiplier: float = 1.0
    mesh: Any = None

    def __call__(
        self,
        inputs: jax.Array,  # [B, T, D]
        mask: jax.Array,  # [B, 1, T, T] or [B, 1, 1, T]
        padding_mask: Optional[jax.Array],
        layer_memory: Optional[KVMemory],
    ) -> DecoderOutput:
        """Transforms input embedding sequences to output embedding sequences."""

        def layer_norm(x):
            return hk_rms_norm(x)

        if self.shard_activations:
            sharding = P(self.data_axis, None, self.model_axis)
        else:
            sharding = P(self.data_axis, None)
        h = with_sharding_constraint(inputs, sharding)

        attn_output = MHABlock(
            num_q_heads=self.num_q_heads,
            num_kv_heads=self.num_kv_heads,
            key_size=self.key_size,
            attn_output_multiplier=self.attn_output_multiplier,
            mesh=self.mesh,
            data_axis=self.data_axis,
            model_axis=self.model_axis,
        )(layer_norm(h), mask, layer_memory)
        h_attn = attn_output.embeddings

        h_attn = layer_norm(h_attn)
        h += h_attn
        h = with_sharding_constraint(h, sharding)

        def base_dense_block(h):
            h = DenseBlock(
                num_q_heads=self.num_q_heads,
                num_kv_heads=self.num_kv_heads,
                key_size=self.key_size,
                widening_factor=self.widening_factor,
                sharding_constraint=False,
                mesh=self.mesh,
            )(h)
            return h

        if self.num_experts > 1:
            rank_logger.debug("Using MoE!")
            router = Router(
                num_selected_experts=self.num_selected_experts,
                shard_activations=self.shard_activations,
                data_axis=self.data_axis,
                model_axis=self.model_axis,
                mesh=self.mesh,
            )
            h_dense = MoELayer(
                num_experts=self.num_experts,
                mesh=self.mesh,
                layer_fn=base_dense_block,
                router=router,
                shard_activations=self.shard_activations,
                data_axis=self.data_axis,
                model_axis=self.model_axis,
            )(layer_norm(h), padding_mask)
        else:
            h_dense = base_dense_block(layer_norm(h))

        h_dense = layer_norm(h_dense)
        h += h_dense
        h = with_sharding_constraint(h, sharding)

        return DecoderOutput(
            embeddings=h,
            memory=attn_output.memory,
        )


class LanguageModelOutput(NamedTuple):
    logits: jax.Array
    model_state: Any


class InOutEmbed(hk.Embed):
    """Module for embedding tokens in a low-dimensional space."""

    def __init__(
        self,
        vocab_size: Optional[int] = None,
        embed_dim: Optional[int] = None,
        sharding: Optional[P] = None,
        name: Optional[str] = None,
    ):
        super().__init__(
            vocab_size=vocab_size,
            embed_dim=embed_dim,
            name=name,
        )
        self.sharding = sharding

    @property
    def embeddings(self):
        embed_mat = hk.get_parameter(
            "embeddings",
            [self.vocab_size, self.embed_dim],
            dtype=jnp.float32,
            init=hk.initializers.Constant(0),
        )
        if self.sharding:
            embed_mat = with_sharding_constraint(embed_mat, self.sharding)
        return embed_mat

    def decode(
        self,
        inputs: jax.Array,
    ) -> jax.Array:
        return jnp.dot(inputs, self.embeddings.T.astype(inputs.dtype))


@dataclass
class LanguageModelConfig:
    """An autoregressive transformer-based language model."""

    model: Optional[TransformerConfig]
    vocab_size: int
    pad_token: int
    eos_token: int
    sequence_len: int
    model_size: int = 0
    embedding_init_scale: float = 1.0
    embedding_multiplier_scale: float = 1.0
    output_multiplier_scale: float = 1.0
    name: Optional[str] = None
    fprop_dtype: Any = jnp.bfloat16
    model_type: Optional[str] = None
    init_scale_override: Optional[float] = None
    shard_embeddings: bool = True

    _initialized = False

    def initialize(self):
        # We cannot specify [] as a default value (it is mutable), hence None.
        model_config = self.model
        assert self.init_scale_override is None, (
            "Overriding model initialize scale is supported only for predefined models."
        )
        if self.model_size == 0:
            self.model_size = model_config.emb_size
        assert self.model is not None, "Model could not be initialized."
        self._initialized = True
        return self

    def make(self, *args, **kwargs):
        if not self._initialized:
            logger.warning(
                f"LanguageModel {self.name} is not initialized. Initializing for one replica."
            )
            self.initialize()

        return LanguageModel(
            model=self.model.make(*args, **kwargs),
            config=self,
            fprop_dtype=self.fprop_dtype,
            mesh=kwargs.get("mesh", None),
        )

    def partition_rules(self):
        return LM_PARTITION_RULES + self.model.partition_rules()


def layer_norm(x, model):
    return hk_rms_norm(x)


@dataclass
class LanguageModel(hk.Module):
    """An autoregressive transformer-based language model."""

    model: "Transformer"
    config: LanguageModelConfig
    fprop_dtype: Any = jnp.bfloat16
    name: Optional[str] = None
    mesh: Any = None

    def __call__(
        self,
        tokens: jax.Array,
        memory: Optional[Memory] = None,
        *,
        batch: Dict[str, jax.Array] = {},
        last_hid_only: bool = False,
        length: Optional[jax.Array] = None,
    ) -> LanguageModelOutput:
        """Forward pass, producing a sequence of logits."""
        del batch  # Unused.

        config = self.config

        input_mask = jnp.greater(tokens, config.pad_token)

        # Embed the input tokens and positions.
        in_out_embed = InOutEmbed(
            self.config.vocab_size,
            embed_dim=self.config.model_size,
            sharding=P(None, ("data", "model")),
        )
        input_embeddings = in_out_embed(tokens).astype(config.fprop_dtype)
        input_embeddings = with_sharding_constraint(
            input_embeddings, P("data", None, self.model.model_axis)
        )
        input_embeddings *= config.embedding_multiplier_scale

        model_output = self.model(
            input_embeddings,
            input_mask,
            memory=memory,
        )  # [B, T, D]
        embeddings, model_state = model_output.embeddings, model_output.memory
        if self.model.shard_activations:
            embeddings = with_sharding_constraint(
                embeddings, P("data", None, self.model.model_axis)
            )
        else:
            embeddings = with_sharding_constraint(embeddings, P("data", None))
        rank_logger.debug(f"Final embedding shape: {embeddings.shape}")
        embeddings = layer_norm(embeddings, self.model)
        assert embeddings.dtype == self.fprop_dtype

        if last_hid_only:
            last_step = jnp.maximum(jnp.sum(input_mask.astype(jnp.int32), axis=1) - 1, 0)
            last_hid = jax.vmap(lambda x, i: x[i], in_axes=0, out_axes=0)(embeddings, last_step)
            return last_hid

        if length is not None:
            last_step = jnp.maximum(length.astype(jnp.int32) - 1, 0)
            embeddings = jax.vmap(lambda x, i: x[i], in_axes=0, out_axes=0)(embeddings, last_step)
            embeddings = jnp.expand_dims(embeddings, axis=1)

        # Decode the embeddings (here, we use tied weights).
        rank_logger.info(embeddings.shape)
        out = in_out_embed.decode(embeddings)
        rank_logger.info(out.shape)
        out *= config.output_multiplier_scale

        if self.model.shard_activations:
            out = with_sharding_constraint(out, P("data", None, self.model.model_axis))
        else:
            out = with_sharding_constraint(out, P("data", None))

        return LanguageModelOutput(
            logits=out,
            model_state=model_state,
        )

    def init_memory(self, batch_size: int, seq_len: int, dtype=jnp.bfloat16):
        return self.model.init_memory(batch_size=batch_size, sequence_len=seq_len, dtype=dtype)

    def prefill_memory(self, prompts, memory):
        # Pad to the left and right align?
        # Basically assume prompt is already padded
        model_output = self(prompts, memory=memory)
        return model_output.logits, model_output.model_state


@dataclass
class Transformer(hk.Module):
    """A transformer stack."""

    num_q_heads: int
    num_kv_heads: int
    key_size: int
    widening_factor: float
    init_scale: float
    mesh: Any
    attn_output_multiplier: float
    shard_activations: bool
    num_layers: int
    # MoE
    num_experts: int
    num_selected_experts: int
    name: Optional[str] = None

    # Used for activation sharding
    data_axis: Union[str, Tuple[str, ...]] = "data"
    model_axis: Union[str, Tuple[str, ...]] = "model"

    def init_memory(self, batch_size: int, sequence_len: int, dtype=jnp.bfloat16):
        return Memory(
            layers=init_layer_memories(
                batch_size,
                sequence_len,
                self.num_kv_heads,
                self.key_size,
                self.num_layers,
                step=jnp.zeros(batch_size, dtype=jnp.int32),
                dtype=dtype,
            ),
        )

    def __call__(
        self,
        embeddings: jax.Array,  # [B, T, D]
        mask: jax.Array,  # [B, T]
        memory: Optional[Memory],
    ) -> TransformerOutput:
        """Transforms input embedding sequences to output embedding sequences."""

        fprop_dtype = embeddings.dtype
        _, seq_len, model_size = embeddings.shape
        padding_mask = mask.copy()
        mask = mask[:, None, None, :]  # [B, H=1, T'=1, T]

        # Compute causal mask for autoregressive sequence modelling.
        causal_mask = jnp.tril(jnp.ones((1, 1, seq_len, seq_len))).astype(
            fprop_dtype
        )  # [B=1, H=1, T, T]
        mask = mask * causal_mask  # [B, H=1, T, T]

        h = embeddings
        kv_memories = []

        def block(
            h,
            mask,
            padding_mask,
            memory,
            layer_index: Optional[int] = None,
            widening_factor: Optional[int] = None,
            name: Optional[str] = None,
        ) -> DecoderOutput:
            return DecoderLayer(
                num_q_heads=self.num_q_heads,
                num_kv_heads=self.num_kv_heads,
                key_size=self.key_size,
                widening_factor=widening_factor or self.widening_factor,
                num_layers=self.num_layers,
                mesh=self.mesh,
                data_axis=self.data_axis,
                model_axis=self.model_axis,
                attn_output_multiplier=self.attn_output_multiplier,
                shard_activations=self.shard_activations,
                # MoE.
                num_experts=self.num_experts,
                num_selected_experts=self.num_selected_experts,
                name=name,
                layer_index=layer_index,
            )(
                h,
                mask,
                padding_mask,
                memory,
            )

        for i in range(self.num_layers):
            decoder_output = block(
                h,
                mask,
                padding_mask,
                memory.layers[i] if memory else None,
                layer_index=i,
                name=f"decoder_layer_{i}",
            )
            h, new_kv_memory = (
                decoder_output.embeddings,
                decoder_output.memory,
            )
            kv_memories.append(new_kv_memory)

        return TransformerOutput(
            embeddings=h,
            memory=Memory(layers=kv_memories),
        )

model.py中可以获取到的其他相关信息:

  • 3140 亿参数(314B parameters)

  • 8 个专家的混合体(Mixture of 8 Experts)

  • 每个 token 使用 2 个专家(2 experts used per token)

  • 64 层(64 layers)

  • 查询的 48 个注意力头(48 attention heads for queries)

  • 键/值的 8 个注意力头(8 attention heads for keys/values)

  • 嵌入大小:6144(embeddings size: 6,144)

  • 旋转嵌入(rotary embeddings, RoPE)

  • SentencePiece 分词器;131,072 个令牌(SentencePiece tokenizer; 131,072 tokens)

  • 支持激活分片和 8 位量化(Supports activation sharding and 8-bit quantization)

  • 最大序列长度(上下文):8192 个 token(Max seq length (context): 8,192 tokens)

3.推理grok-1模型需要多大显存?

        那么我们大致算一下,得多少显存才能够推理grok-1?

        以Llama2为基准,计算:

  • 全精度llama2 7B最低显存要求:28GB
  • 全精度llama2 13B最低显存要求:52GB
  • 全精度llama2 70B最低显存要求:280GB

  • 16精度llama2 7B预测最低显存要求:14GB
  • 16精度llama2 13B预测最低显存要求:26GB
  • 16精度llama2 70B预测最低显存要求:140GB

  • 8精度llama2 7B预测最低显存要求:7GB
  • 8精度llama2 13B预测最低显存要求:13GB
  • 8精度llama2 70B预测最低显存要求:70GB

  • 4精度llama2 7B预测最低显存要求:3.5GB
  • 4精度llama2 13B预测最低显存要求:6.5GB
  • 4精度llama2 70B预测最低显存要求:35GB

推理计算可得:

  • 314B的grok-1模型FP8下大约需要314G
  • 314B的grok-1模型FP16下大约需要628G
  • 314B的grok-1模型FP32下大约需要1256G

        那么也就是说在 FP16 下大约需要 630GB 的显存,可能会达到 700GB。使用 8 个 H100 是否能运行还是未知数,我认为在它被优化之前,你无法在 CPU 上运行它。在 FP16 精度下,Grok-1 模型大约需要 630GB 至 700GB 的显存。即便配置了 8 个 NVIDIA H100 GPU,能否成功运行该模型仍不确定。

        现在回过头再看看开源的grok-1模型,只能说普通人真的玩不起。。。

4.grok-1开源意味着什么? 

        但是针对马斯克此前一直诟病OpenAI不open的心态,马斯克开源GROK-1模型是一个令人振奋和令人期待的举措。GROK-1模型是一种语言模型,可以生成各种类型的文本,包括文章、故事、代码等。通过开源GROK-1模型,马斯克为研究人员、工程师和开发者提供了一个强大的工具,可以帮助他们在各种领域进行创新和研究。

        开源GROK-1模型的好处是多方面的。首先,它可以促进知识共享和合作。开源意味着任何人都可以访问和使用这个模型,这将为研究和开发带来更多可能性和创新。其次,GROK-1模型的开源可以加快技术进步的速度。研究人员和开发者可以在此基础上进行改进和优化,从而推动整个领域的进步。最后,开源GROK-1模型还可以促进公众对人工智能技术的理解和认识。公众可以通过访问和使用这个模型,更好地了解人工智能的应用和潜力。

        然而,开源GROK-1模型也存在一些挑战和问题。首先,模型的开源可能导致滥用和误用。人工智能技术在某些情况下可能被用于恶意目的,因此需要有相关的监管和控制机制。其次,模型的开源可能引发知识产权问题。开源意味着任何人都可以使用和修改这个模型,这可能对原创者的利益造成不利影响。最后,模型的开源可能导致技术壁垒的降低,进而减少了商业机会和竞争力。

        总体而言,马斯克开源GROK-1模型是一个积极的举措,有助于推动人工智能技术的发展和应用。然而,在推进开源的同时,我们也需要认真考虑和解决相关的问题和挑战。

        xAI官网也表示Grok仍然是一个非常早期的测试版产品,是“我们在数月的训练中所能做到的最好产品,期望在您的帮助下每周都能快速改进”。
 

5.最后,让我们一同期待开源grok-1模型的训练代码!

版权声明:本文为博主原创文章,遵循 CC 4.0 BY-SA 版权协议,转载请附上原文出处链接和本声明。
本文链接:https://blog.csdn.net/qq_38308388/article/details/136812387

智能推荐

oracle 12c 集群安装后的检查_12c查看crs状态-程序员宅基地

文章浏览阅读1.6k次。安装配置gi、安装数据库软件、dbca建库见下:http://blog.csdn.net/kadwf123/article/details/784299611、检查集群节点及状态:[root@rac2 ~]# olsnodes -srac1 Activerac2 Activerac3 Activerac4 Active[root@rac2 ~]_12c查看crs状态

解决jupyter notebook无法找到虚拟环境的问题_jupyter没有pytorch环境-程序员宅基地

文章浏览阅读1.3w次,点赞45次,收藏99次。我个人用的是anaconda3的一个python集成环境,自带jupyter notebook,但在我打开jupyter notebook界面后,却找不到对应的虚拟环境,原来是jupyter notebook只是通用于下载anaconda时自带的环境,其他环境要想使用必须手动下载一些库:1.首先进入到自己创建的虚拟环境(pytorch是虚拟环境的名字)activate pytorch2.在该环境下下载这个库conda install ipykernelconda install nb__jupyter没有pytorch环境

国内安装scoop的保姆教程_scoop-cn-程序员宅基地

文章浏览阅读5.2k次,点赞19次,收藏28次。选择scoop纯属意外,也是无奈,因为电脑用户被锁了管理员权限,所有exe安装程序都无法安装,只可以用绿色软件,最后被我发现scoop,省去了到处下载XXX绿色版的烦恼,当然scoop里需要管理员权限的软件也跟我无缘了(譬如everything)。推荐添加dorado这个bucket镜像,里面很多中文软件,但是部分国外的软件下载地址在github,可能无法下载。以上两个是官方bucket的国内镜像,所有软件建议优先从这里下载。上面可以看到很多bucket以及软件数。如果官网登陆不了可以试一下以下方式。_scoop-cn

Element ui colorpicker在Vue中的使用_vue el-color-picker-程序员宅基地

文章浏览阅读4.5k次,点赞2次,收藏3次。首先要有一个color-picker组件 <el-color-picker v-model="headcolor"></el-color-picker>在data里面data() { return {headcolor: ’ #278add ’ //这里可以选择一个默认的颜色} }然后在你想要改变颜色的地方用v-bind绑定就好了,例如:这里的:sty..._vue el-color-picker

迅为iTOP-4412精英版之烧写内核移植后的镜像_exynos 4412 刷机-程序员宅基地

文章浏览阅读640次。基于芯片日益增长的问题,所以内核开发者们引入了新的方法,就是在内核中只保留函数,而数据则不包含,由用户(应用程序员)自己把数据按照规定的格式编写,并放在约定的地方,为了不占用过多的内存,还要求数据以根精简的方式编写。boot启动时,传参给内核,告诉内核设备树文件和kernel的位置,内核启动时根据地址去找到设备树文件,再利用专用的编译器去反编译dtb文件,将dtb还原成数据结构,以供驱动的函数去调用。firmware是三星的一个固件的设备信息,因为找不到固件,所以内核启动不成功。_exynos 4412 刷机

Linux系统配置jdk_linux配置jdk-程序员宅基地

文章浏览阅读2w次,点赞24次,收藏42次。Linux系统配置jdkLinux学习教程,Linux入门教程(超详细)_linux配置jdk

随便推点

matlab(4):特殊符号的输入_matlab微米怎么输入-程序员宅基地

文章浏览阅读3.3k次,点赞5次,收藏19次。xlabel('\delta');ylabel('AUC');具体符号的对照表参照下图:_matlab微米怎么输入

C语言程序设计-文件(打开与关闭、顺序、二进制读写)-程序员宅基地

文章浏览阅读119次。顺序读写指的是按照文件中数据的顺序进行读取或写入。对于文本文件,可以使用fgets、fputs、fscanf、fprintf等函数进行顺序读写。在C语言中,对文件的操作通常涉及文件的打开、读写以及关闭。文件的打开使用fopen函数,而关闭则使用fclose函数。在C语言中,可以使用fread和fwrite函数进行二进制读写。‍ Biaoge 于2024-03-09 23:51发布 阅读量:7 ️文章类型:【 C语言程序设计 】在C语言中,用于打开文件的函数是____,用于关闭文件的函数是____。

Touchdesigner自学笔记之三_touchdesigner怎么让一个模型跟着鼠标移动-程序员宅基地

文章浏览阅读3.4k次,点赞2次,收藏13次。跟随鼠标移动的粒子以grid(SOP)为partical(SOP)的资源模板,调整后连接【Geo组合+point spirit(MAT)】,在连接【feedback组合】适当调整。影响粒子动态的节点【metaball(SOP)+force(SOP)】添加mouse in(CHOP)鼠标位置到metaball的坐标,实现鼠标影响。..._touchdesigner怎么让一个模型跟着鼠标移动

【附源码】基于java的校园停车场管理系统的设计与实现61m0e9计算机毕设SSM_基于java技术的停车场管理系统实现与设计-程序员宅基地

文章浏览阅读178次。项目运行环境配置:Jdk1.8 + Tomcat7.0 + Mysql + HBuilderX(Webstorm也行)+ Eclispe(IntelliJ IDEA,Eclispe,MyEclispe,Sts都支持)。项目技术:Springboot + mybatis + Maven +mysql5.7或8.0+html+css+js等等组成,B/S模式 + Maven管理等等。环境需要1.运行环境:最好是java jdk 1.8,我们在这个平台上运行的。其他版本理论上也可以。_基于java技术的停车场管理系统实现与设计

Android系统播放器MediaPlayer源码分析_android多媒体播放源码分析 时序图-程序员宅基地

文章浏览阅读3.5k次。前言对于MediaPlayer播放器的源码分析内容相对来说比较多,会从Java-&amp;amp;gt;Jni-&amp;amp;gt;C/C++慢慢分析,后面会慢慢更新。另外,博客只作为自己学习记录的一种方式,对于其他的不过多的评论。MediaPlayerDemopublic class MainActivity extends AppCompatActivity implements SurfaceHolder.Cal..._android多媒体播放源码分析 时序图

java 数据结构与算法 ——快速排序法-程序员宅基地

文章浏览阅读2.4k次,点赞41次,收藏13次。java 数据结构与算法 ——快速排序法_快速排序法