NickGnd

Understanding `Nx.dot/6`: contracting and batch axes

Hello everyone

I’m watching the great video “Let’s build GPT: from scratch, in code, spelled out.” by Andrej Karpathy.

All the code is written in python (using numpy and torch) and I’m trying to rewrite it in Nx/Axon.

I’m now at minute 55, when a matrix multiplication between 2 tensors of different dimensions is executed.

I was able to obtain the same result with Nx.dot/6 but only when specifying the contracting and batch axes (see docs). I searched a bit online, but I’m not fully sure I got it correctly.

Let’s take this silly example in Python (commented the result/values):

import numpy as np
import torch
from torch.nn import functional as F

# B -> batch, T -> time, C -> channel
B,T,C = 4,8,2

x = np.arange(0, 64, 1).reshape((B, T, C))
# array([[[ 0,  1],
#         [ 2,  3],
#         [ 4,  5],
#         [ 6,  7],
#         [ 8,  9],
#         [10, 11],
#         [12, 13],
#         [14, 15]],

#        [[16, 17],
#         [18, 19],
#         [20, 21],
#         [22, 23],
#         [24, 25],
#         [26, 27],
#         [28, 29],
#         [30, 31]],

#        [[32, 33],
#         [34, 35],
#         [36, 37],
#         [38, 39],
#         [40, 41],
#         [42, 43],
#         [44, 45],
#         [46, 47]],

#        [[48, 49],
#         [50, 51],
#         [52, 53],
#         [54, 55],
#         [56, 57],
#         [58, 59],
#         [60, 61],
#         [62, 63]]])


tril = torch.tril(torch.ones(T, T))
wei = torch.zeros((T,T))
wei = wei.masked_fill(tril == 0, float('-inf'))
wei = F.softmax(wei, dim=-1)
# tensor([[1.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.5000, 0.5000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.3333, 0.3333, 0.3333, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.2500, 0.2500, 0.2500, 0.2500, 0.0000, 0.0000, 0.0000, 0.0000],
#         [0.2000, 0.2000, 0.2000, 0.2000, 0.2000, 0.0000, 0.0000, 0.0000],
#         [0.1667, 0.1667, 0.1667, 0.1667, 0.1667, 0.1667, 0.0000, 0.0000],
#         [0.1429, 0.1429, 0.1429, 0.1429, 0.1429, 0.1429, 0.1429, 0.0000],
#         [0.1250, 0.1250, 0.1250, 0.1250, 0.1250, 0.1250, 0.1250, 0.1250]])


res = wei @ x
# tensor([[[ 0.0000,  1.0000],
#          [ 1.0000,  2.0000],
#          [ 2.0000,  3.0000],
#          [ 3.0000,  4.0000],
#          [ 4.0000,  5.0000],
#          [ 5.0000,  6.0000],
#          [ 6.0000,  7.0000],
#          [ 7.0000,  8.0000]],

#         [[16.0000, 17.0000],
#          [17.0000, 18.0000],
#          [18.0000, 19.0000],
#          [19.0000, 20.0000],
#          [20.0000, 21.0000],
#          [21.0000, 22.0000],
#          [22.0000, 23.0000],
#          [23.0000, 24.0000]],

#         [[32.0000, 33.0000],
#          [33.0000, 34.0000],
#          [34.0000, 35.0000],
#          [35.0000, 36.0000],
#          [36.0000, 37.0000],
#          [37.0000, 38.0000],
#          [38.0000, 39.0000],
#          [39.0000, 40.0000]],

#         [[48.0000, 49.0000],
#          [49.0000, 50.0000],
#          [50.0000, 51.0000],
#          [51.0000, 52.0000],
#          [52.0000, 53.0000],
#          [53.0000, 54.0000],
#          [54.0000, 55.0000],
#          [55.0000, 56.0000]]], dtype=torch.float64)

res.shape
# torch.Size([4, 8, 2])

The relevant information are:

x shape is (B,T,C)
wei shape is (T, T)
res shape is (B, T, C)

And apparently the matrix multiplication in torch wei @ x behaves in a way that automatically adds the B (batch) dimension to wei by broadcasting it.

I think that’s what the torch docs mean with:

The non-matrix (i.e. batch) dimensions are broadcasted (and thus must be broadcastable). For example, if input is a (j×1×n×n) tensor and other is a (k×n×n) tensor, out will be a (j×k×n×n) tensor.

I could get the same result with elixir Nx after a bit of trial and errors:

Mix.install(
  [
    {:nx, "~> 0.6", override: true},
    {:axon, "~> 0.5"},
  ],
  config: [nx: [default_backend: EXLA.Backend]]
)

{b, t, c} = {4, 8, 2}

x = Nx.iota({4, 8, 2})
#Nx.Tensor<
#   s64[4][8][2]
#   EXLA.Backend<host:0, 0.668616701.3656777744.246793>
#   [
#     [
#       [0, 1],
#       [2, 3],
#       [4, 5],
#       [6, 7],
#       [8, 9],
#       [10, 11],
#       [12, 13],
#       [14, 15]
#     ],
#     [
#       [16, 17],
#       [18, 19],
#       [20, 21],
#       [22, 23],
#       [24, 25],
#       [26, 27],
#       [28, 29],
#       [30, 31]
#     ],
#     [
#       [32, 33],
#       [34, 35],
#       [36, 37],
#       [38, 39],
#       [40, 41],
#       [42, 43],
#       [44, 45],
#       [46, 47]
#     ],
#     [
#       [48, 49],
#       ...
#     ]
#   ]
# >

wei =
  Nx.broadcast(Nx.Constants.neg_infinity(), {t, t})
  |> Nx.triu(k: 1)
  |> Axon.Activations.softmax()
# #Nx.Tensor<
#   f32[8][8]
#   EXLA.Backend<host:0, 0.668616701.3656777744.246805>
#   [
#     [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
#     [0.5, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
#     [0.3333333432674408, 0.3333333432674408, 0.3333333432674408, 0.0, 0.0, 0.0, 0.0, 0.0],
#     [0.25, 0.25, 0.25, 0.25, 0.0, 0.0, 0.0, 0.0],
#     [0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.0, 0.0, 0.0],
#     [0.1666666716337204, 0.1666666716337204, 0.1666666716337204, 0.1666666716337204, 0.1666666716337204, 0.1666666716337204, 0.0, 0.0],
#     [0.1428571492433548, 0.1428571492433548, ...],
#     ...
#   ]
# >


# WRONG, DIFFERENT RESULT! ❌
# res = Nx.dot(wei, x)

# WRONG, DIFFERENT RESULT! ❌
# res = Nx.dot(wei, x) |> Nx.reshape({b, t, c})


# ☝️ Add the batch dimension by broadcasting it
wei_with_batches = Nx.broadcast(wei, {b, t, t})
# #Nx.Tensor<
#   f32[4][8][8]
#   EXLA.Backend<host:0, 0.668616701.3656777744.246806>
#   [
#     [
#       [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
#       [0.5, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
#       [0.3333333432674408, 0.3333333432674408, 0.3333333432674408, 0.0, 0.0, 0.0, 0.0, 0.0],
#       [0.25, 0.25, 0.25, 0.25, 0.0, 0.0, 0.0, 0.0],
#       [0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.0, 0.0, 0.0],
#       [0.1666666716337204, 0.1666666716337204, 0.1666666716337204, 0.1666666716337204, 0.1666666716337204, 0.1666666716337204, 0.0, 0.0],
#       [0.1428571492433548, 0.1428571492433548, ...],
#       ...
#     ],
#     [
#       [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
#       [0.5, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
#       [0.3333333432674408, 0.3333333432674408, 0.3333333432674408, 0.0, 0.0, 0.0, 0.0, 0.0],
#       ...
#     ],
#     ...
#   ]
# >

# WRONG, DIFFERENT RESULT! ❌
# res = Nx.dot(wei_with_batches, x)

# CORRECT! SAME RESULT! ✅
res = Nx.dot(wei_with_batches, [2], [0], x, [1], [0])
# #Nx.Tensor<
#   f32[4][8][2]
#   EXLA.Backend<host:0, 0.668616701.3656777744.246807>
#   [
#     [
#       [0.0, 1.0],
#       [1.0, 2.0],
#       [2.0, 3.0],
#       [3.0, 4.0],
#       [4.0, 5.0],
#       [5.0, 6.000000476837158],
#       [6.000000476837158, 7.000000476837158],
#       [7.0, 8.0]
#     ],
#     [
#       [16.0, 17.0],
#       [17.0, 18.0],
#       [18.0, 19.000001907348633],
#       [19.0, 20.0],
#       [20.0, 21.0],
#       [21.000001907348633, 22.0],
#       [22.000001907348633, 23.0],
#       [23.0, 24.0]
#     ],
#     [
#       [32.0, 33.0],
#       [33.0, 34.0],
#       [34.0, 35.0],
#       [35.0, 36.0],
#       [36.0, 37.0],
#       [37.0, 38.0],
#       [38.0, 39.000003814697266],
#       [39.0, 40.0]
#     ],
#     [
#       [48.0, 49.0],
#       ...
#     ]
#   ]
# >

To recap:

x shape is (B,T,C)
wei shape is (T, T)
wei_with_batches shape is (B, T, T)
res shape is (B, T, C)

In particular, I needed to use the Nx.dot/6 function specifying the contracting and batch axes:

Nx.dot(wei_with_batches, [2], [0], x, [1], [0])

where the contracting axes are:

2nd T in wei_with_batches ((B, T, T)[2])
T in x ((B, T, C)[1])

and the batch axes are:

B in both wei_with_batches and x

Quoting the docs:

The dot product is computed by multiplying the values from t1 given by contract_axes1 against the values from t2 given by contract_axes2 , considering batch axes of batch_axes1 and batch_axes2 . For instance, the first axis in contract_axes1 will be matched against the first axis in contract_axes2 and so on.

The axes given by contract_axes1 and contract_axes2 are effectively removed from the final tensor, which is why they are often called the contraction axes.

Specifying batch axes will compute a vectorized dot product along the given batch dimensions. The length of batch_axes1 and batch_axes2 must match.

So I guess the final res tensor is (B, T, C) because, if we keep the batch dimension on the side for a moment:

(T, T) @ (T, C) = (T, C), then adding back the B batch dimension → (B, T, C)

(kinda makes sense but I think there is a better way for explaining it ).

To conclude, I’d like to understand if my reasoning above makes sense and if this is the correct way to replicate the Python snippet or if there is a better way for doing this.

Thank youuuu

3 comments

/nx #matrix #python-numpy #torchx

5 694 3

2023-09-22 15:36:53 UTC

Marked As Solved

polvalente

Nx Core Team

Nx.dot doesn’t generally apply broadcasting (although some cases such as dot(scalar, non-scalar) tensor do).

That being said, you can use a combination of Nx.new_axis + Nx.tile on your weights tensor to mimic that.

That is:

w -> {T, T}
x -> {B, T, C}

then

w2 = Nx.new_axis(w) -> {1, T, T}
Nx.tile(w2, [B, 1, 1]) -> {B, T, T}

However, Nx 0.6 brought vectorization, and with that you can more easily deal with the batched case:

x -> {B, T, C}
w -> {K, T}

vec_x = Nx.vectorize(x, :some_name) # vectorized[some_name: B] {T, C}
vec_result = Nx.dot(w, [1], x, [0]) # vectorized[some_name: B] {K, C}
# Note: vec_result = Nx.dot(w, x) returns the same result :)
result = Nx.devectorize(vec_result, keep_names: false) # {B, K, C}

Example with values:

iex(5)> x            
#Nx.Tensor<
  s64[4][2][3]
  [
    [
      [0, 1, 2],
      [3, 4, 5]
    ],
    [
      [6, 7, 8],
      [9, 10, 11]
    ],
    [
      [12, 13, 14],
      [15, 16, 17]
    ],
    [
      [18, 19, 20],
      [21, 22, 23]
    ]
  ]
>
iex(6)> w = Nx.iota({5, 2})
#Nx.Tensor<
  s64[5][2]
  [
    [0, 1],
    [2, 3],
    [4, 5],
    [6, 7],
    [8, 9]
  ]
>
iex(7)> Nx.dot(w, Nx.vectorize(x, :some_name))
#Nx.Tensor<
  vectorized[some_name: 4]
  s64[5][3]
  [
    [
      [3, 4, 5],
      [9, 14, 19],
      [15, 24, 33],
      [21, 34, 47],
      [27, 44, 61]
    ],
    [
      [9, 10, 11],
      [39, 44, 49],
      [69, 78, 87],
      [99, 112, 125],
      [129, 146, 163]
    ],
    [
      [15, 16, 17],
      [69, 74, 79],
      [123, 132, 141],
      [177, 190, 203],
      [231, 248, 265]
    ],
    [
      [21, 22, 23],
      [99, 104, ...],
      ...
    ]
  ]
>
iex(8)> |> Nx.devectorize(keep_names: false)
#Nx.Tensor<
  s64[4][5][3]
  [
    [
      [3, 4, 5],
      [9, 14, 19],
      [15, 24, 33],
      [21, 34, 47],
      [27, 44, 61]
    ],
    [
      [9, 10, 11],
      [39, 44, 49],
      [69, 78, 87],
      [99, 112, 125],
      [129, 146, 163]
    ],
    [
      [15, 16, 17],
      [69, 74, 79],
      [123, 132, 141],
      [177, 190, 203],
      [231, 248, 265]
    ],
    [
      [21, 22, 23],
      [99, 104, ...],
      ...
    ]
  ]
>

Post #2

Also Liked

NickGnd

Hey @polvalente thank you so much for the fast reply and to provide me some examples too, really helpful!

What you suggested:

w2 = Nx.new_axis(w) # -> {1, T, T}
Nx.tile(w2, [B, 1, 1]) # -> {B, T, T}

is basically the same of what I did with:

wei = Nx.broadcast(wei, {b, t, t})

Why would you prefer the combination of Nx.new_axis and Nx.tile over Nx.broadcasting?

However, Nx 0.6 brought vectorization, and with that you can more easily deal with the batched case:

Vectorization for the WIN!
I remember I saw the section in the Nx guide but I didn’t fully get it at the time (still now, but better ), and oh wow, it really fits this case perfectly, thanks for bringing it up.

Thanks again for chiming in and for the great work you and the nx team are doing

Post #3

polvalente

Nx Core Team

You’re right! I just brainfarted for a bit because Nx.broadcast(w, x) doesn’t work directly.

You can do something like:

iex(4)> t1.shape
{2, 3}
iex(5)> t2.shape
{5, 3, 4}
iex(6)> Nx.broadcast(t1, Tuple.insert_at(t1.shape, 0, Nx.axis_size(t2, 0)))
#Nx.Tensor<
  s64[5][2][3]
  ...
>

Note that inside defn you’ll probably need to define a separate deftransformp for applying this broadcast.
Vectorization will come with its own separate set of difficulties if you want to support a function that receives a maybe vectorized tensor, though, so each approach has its own trade-offs.

Post #4