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Rename the .r functions to .dims so as to be a bit more explicit. (#220)

This commit is contained in:
Laurent Mazare 2023-07-22 11:39:27 +02:00 committed by GitHub
parent 52c5d8c087
commit 43c7223292
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GPG Key ID: 4AEE18F83AFDEB23
18 changed files with 56 additions and 50 deletions
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2
candle-core/src/cpu_backend.rs
View File

@ -1688,7 +1688,7 @@ impl BackendStorage for CpuStorage {
fn embedding(&self, ids_l: &Layout, rhs: &Self, rhs_l: &Layout) -> Result<Self> {
let ids = self.as_slice::<u32>()?;
let (vocab_size, hidden_size) = rhs_l.shape().r2()?;
let (vocab_size, hidden_size) = rhs_l.shape().dims2()?;
Embedding {
vocab_size,
hidden_size,

2
candle-core/src/cuda_backend.rs
View File

@ -620,7 +620,7 @@ impl<'a> Map1 for Embedding<'a> {
let shape = ids_l.shape();
let (v_size, h_size) = rhs_l
.shape()
.r2()
.dims2()
.map_err(|e| CudaError::WrappedError(Box::new(e)))
.w()?;
let dims = shape.dims();

18
candle-core/src/shape.rs
View File

@ -87,6 +87,12 @@ macro_rules! extract_dims {
}
}
}
impl crate::Tensor {
pub fn $fn_name(&self) -> Result<$out_type> {
self.shape().$fn_name()
}
}
impl std::convert::TryInto<$out_type> for Shape {
type Error = crate::Error;
fn try_into(self) -> std::result::Result<$out_type, Self::Error> {
@ -328,23 +334,23 @@ impl<D1: Dim, D2: Dim, D3: Dim> Dims for (D1, D2, D3) {
}
}
extract_dims!(r0, 0, |_: &Vec<usize>| (), ());
extract_dims!(r1, 1, |d: &[usize]| d[0], usize);
extract_dims!(r2, 2, |d: &[usize]| (d[0], d[1]), (usize, usize));
extract_dims!(dims0, 0, |_: &Vec<usize>| (), ());
extract_dims!(dims1, 1, |d: &[usize]| d[0], usize);
extract_dims!(dims2, 2, |d: &[usize]| (d[0], d[1]), (usize, usize));
extract_dims!(
r3,
dims3,
3,
|d: &[usize]| (d[0], d[1], d[2]),
(usize, usize, usize)
);
extract_dims!(
r4,
dims4,
4,
|d: &[usize]| (d[0], d[1], d[2], d[3]),
(usize, usize, usize, usize)
);
extract_dims!(
r5,
dims5,
5,
|d: &[usize]| (d[0], d[1], d[2], d[3], d[4]),
(usize, usize, usize, usize, usize)

12
candle-core/src/tensor.rs
View File

@ -772,7 +772,7 @@ impl Tensor {
/// Applies a 1D convolution over the input tensor.
pub fn conv1d(&self, kernel: &Self, padding: usize, stride: usize) -> Result<Self> {
let (c_out, c_in_k, k_size) = kernel.shape().r3()?;
let (c_out, c_in_k, k_size) = kernel.dims3()?;
let (b_size, c_in, l_in) = match *self.dims() {
[b_size, c_in, l_in] => (Some(b_size), c_in, l_in),
[c_in, l_in] => (None, c_in, l_in),
@ -931,8 +931,8 @@ impl Tensor {
.bt())?
}
let ids_shape = ids.shape();
let seq_len = ids_shape.r1()?;
let (_, hidden_size) = rhs.shape().r2()?;
let seq_len = ids_shape.dims1()?;
let (_, hidden_size) = rhs.dims2()?;
let storage = ids
.storage()
.embedding(ids.layout(), &rhs.storage(), rhs.layout())?;
@ -1013,7 +1013,7 @@ impl Tensor {
// The number of element in indexes must match the dimension on which the add is
// performed on the source tensor (and the index values from `indexes` are taken from
// the target tensor self)
mismatch || source_dims[dim] != indexes.shape().r1()?
mismatch || source_dims[dim] != indexes.dims1()?
};
if mismatch {
Err(Error::ShapeMismatchBinaryOp {
@ -1144,7 +1144,7 @@ impl Tensor {
/// Returns the data contained in a 2D tensor as a vector of vector of scalar values.
pub fn to_vec2<S: crate::WithDType>(&self) -> Result<Vec<Vec<S>>> {
let (dim1, dim2) = self.shape().r2()?;
let (dim1, dim2) = self.dims2()?;
let from_cpu_storage = |cpu_storage: &crate::CpuStorage| {
let data = S::cpu_storage_as_slice(cpu_storage)?;
let mut rows = vec![];
@ -1164,7 +1164,7 @@ impl Tensor {
/// Returns the data contained in a 3D tensor.
pub fn to_vec3<S: crate::WithDType>(&self) -> Result<Vec<Vec<Vec<S>>>> {
let (dim1, dim2, dim3) = self.shape().r3()?;
let (dim1, dim2, dim3) = self.dims3()?;
let from_cpu_storage = |cpu_storage: &crate::CpuStorage| {
let data = S::cpu_storage_as_slice(cpu_storage)?;
let mut top_rows = vec![];

10
candle-core/tests/tensor_tests.rs
View File

@ -4,7 +4,7 @@ use test_utils::to_vec3_round;
fn zeros(device: &Device) -> Result<()> {
let tensor = Tensor::zeros((5, 2), DType::F32, device)?;
let (dim1, dim2) = tensor.shape().r2()?;
let (dim1, dim2) = tensor.dims2()?;
assert_eq!(dim1, 5);
assert_eq!(dim2, 2);
Ok(())
@ -12,7 +12,7 @@ fn zeros(device: &Device) -> Result<()> {
fn add_mul(device: &Device) -> Result<()> {
let tensor = Tensor::new(&[3f32, 1., 4.], device)?;
let dim1 = tensor.shape().r1()?;
let dim1 = tensor.dims1()?;
assert_eq!(dim1, 3);
let content: Vec<f32> = tensor.to_vec1()?;
assert_eq!(content, [3., 1., 4.]);
@ -28,7 +28,7 @@ fn add_mul(device: &Device) -> Result<()> {
fn tensor_2d(device: &Device) -> Result<()> {
let data = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
let tensor = Tensor::new(data, device)?;
let dims = tensor.shape().r2()?;
let dims = tensor.dims2()?;
assert_eq!(dims, (2, 5));
let content: Vec<Vec<f32>> = tensor.to_vec2()?;
assert_eq!(content, data);
@ -41,7 +41,7 @@ fn binary_op(device: &Device) -> Result<()> {
let data2 = &[[5f32, 5., 5., 5., 5.], [2., 1., 7., 8., 2.]];
let tensor2 = Tensor::new(data2, device)?;
let tensor = (&tensor + (&tensor * &tensor)? / (&tensor + &tensor2))?;
let dims = tensor.shape().r2()?;
let dims = tensor.dims2()?;
assert_eq!(dims, (2, 5));
let content: Vec<Vec<f32>> = tensor.to_vec2()?;
assert_eq!(content[0], [4.125, 1.1666666, 5.7777777, 1.1666666, 7.5]);
@ -56,7 +56,7 @@ fn binary_op(device: &Device) -> Result<()> {
fn transpose(device: &Device) -> Result<()> {
let data = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
let tensor = Tensor::new(data, device)?.t()?;
let dims = tensor.shape().r2()?;
let dims = tensor.dims2()?;
assert_eq!(dims, (5, 2));
assert_eq!(
tensor.to_vec2::<f32>()?,

2
candle-examples/examples/bert/main.rs
View File

@ -161,7 +161,7 @@ fn main() -> Result<()> {
let embeddings = model.forward(&token_ids, &token_type_ids)?;
println!("generated embeddings {:?}", embeddings.shape());
// Apply some avg-pooling by taking the mean embedding value for all tokens (including padding)
let (_n_sentence, n_tokens, _hidden_size) = embeddings.shape().r3()?;
let (_n_sentence, n_tokens, _hidden_size) = embeddings.dims3()?;
let embeddings = (embeddings.sum(1)? / (n_tokens as f64))?;
println!("pooled embeddings {:?}", embeddings.shape());
let mut similarities = vec![];

4
candle-examples/examples/bert/model.rs
View File

@ -87,7 +87,7 @@ impl LayerNorm {
DType::F16 | DType::BF16 => DType::F32,
d => d,
};
let (_bsize, _seq_len, hidden_size) = x.shape().r3()?;
let (_bsize, _seq_len, hidden_size) = x.dims3()?;
let x = x.to_dtype(internal_dtype)?;
let mean_x = (x.sum_keepdim(2)? / hidden_size as f64)?;
let x = x.broadcast_sub(&mean_x)?;
@ -262,7 +262,7 @@ impl BertEmbeddings {
fn forward(&self, input_ids: &Tensor, token_type_ids: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
let (_bsize, seq_len) = input_ids.shape().r2()?;
let (_bsize, seq_len) = input_ids.dims2()?;
let input_embeddings = self.word_embeddings.forward(input_ids)?;
let token_type_embeddings = self.token_type_embeddings.forward(token_type_ids)?;
let mut embeddings = (&input_embeddings + token_type_embeddings)?;

8
candle-examples/examples/falcon/model.rs
View File

@ -182,7 +182,7 @@ impl FalconRotaryEmbedding {
key: &Tensor,
past_kv_len: usize,
) -> Result<(Tensor, Tensor)> {
let (_batch, seq_len, _head_dim) = query.shape().r3()?;
let (_batch, seq_len, _head_dim) = query.dims3()?;
let (cos, sin) = self.cos_sin(MAX_SEQ_LEN, query.device(), query.dtype())?;
let cos = cos.narrow(0, past_kv_len, seq_len)?;
let sin = sin.narrow(0, past_kv_len, seq_len)?;
@ -245,7 +245,7 @@ impl FalconAttention {
}
fn split_heads(&self, fused_qkv: &Tensor) -> Result<(Tensor, Tensor, Tensor)> {
let (b_sz, seq_len, _) = fused_qkv.shape().r3()?;
let (b_sz, seq_len, _) = fused_qkv.dims3()?;
if !self.multi_query {
let fused_qkv = fused_qkv.reshape((b_sz, seq_len, self.num_heads, 3, self.head_dim))?;
let q = fused_qkv.narrow(D::Minus2, 0, 1)?.squeeze(D::Minus2)?;
@ -267,7 +267,7 @@ impl FalconAttention {
let fused_qkv = self.query_key_value.forward(x)?;
let head_dim = self.head_dim;
let (query, key, value) = self.split_heads(&fused_qkv)?;
let (b_sz, seq_len, _, _) = query.shape().r4()?;
let (b_sz, seq_len, _, _) = query.dims4()?;
let query = query
.transpose(1, 2)?
.reshape((b_sz * self.num_heads, seq_len, head_dim))?;
@ -465,7 +465,7 @@ impl Falcon {
}
pub fn forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
let (b_sz, seq_len) = input_ids.shape().r2()?;
let (b_sz, seq_len) = input_ids.dims2()?;
let mut hidden_state = self.word_embeddings.forward(input_ids)?;
let past_kv_len = match &self.blocks[0].self_attention.kv_cache {
Some((k, _)) => k.dim(1)?,

12
candle-examples/examples/llama/model.rs
View File

@ -116,11 +116,11 @@ impl RmsNorm {
let in_dtype = x.dtype();
// This is a no-op if x's dtype is already f32.
let x = x.to_dtype(DType::F32)?;
let (b_sz, seq_len, hidden_size) = x.shape().r3()?;
let (b_sz, seq_len, hidden_size) = x.dims3()?;
let norm_x = (x.sqr()?.sum_keepdim(2)? / hidden_size as f64)?;
let norm_x = norm_x.broadcast_as((b_sz, seq_len, hidden_size))?;
let x_normed = (x / (norm_x + 1e-6)?.sqrt()?)?;
let size = self.scale.shape().r1()?;
let size = self.scale.dims1()?;
let scale = self
.scale
.to_dtype(DType::F32)?
@ -144,7 +144,7 @@ struct CausalSelfAttention {
impl CausalSelfAttention {
fn apply_rotary_emb(&self, x: &Tensor, index_pos: usize) -> Result<Tensor> {
let (b_sz, _, seq_len, n_embd) = x.shape().r4()?;
let (b_sz, _, seq_len, n_embd) = x.dims4()?;
let cos = self.cache.cos.narrow(0, index_pos, seq_len)?;
let sin = self.cache.sin.narrow(0, index_pos, seq_len)?;
let cos = cos.broadcast_as((b_sz, 1, seq_len, n_embd))?;
@ -158,7 +158,7 @@ impl CausalSelfAttention {
fn forward(&self, x: &Tensor, index_pos: usize, block_idx: usize) -> Result<Tensor> {
let x_dtype = x.dtype();
let (b_sz, seq_len, n_embd) = x.shape().r3()?;
let (b_sz, seq_len, n_embd) = x.dims3()?;
let q = self.q_proj.forward(x)?;
let k = self.k_proj.forward(x)?;
let v = self.v_proj.forward(x)?;
@ -219,7 +219,7 @@ impl CausalSelfAttention {
if n_rep == 1 {
Ok(x)
} else {
let (b_sz, n_kv_head, seq_len, head_dim) = x.shape().r4()?;
let (b_sz, n_kv_head, seq_len, head_dim) = x.dims4()?;
let x = x
.unsqueeze(2)?
.expand((b_sz, n_kv_head, n_rep, seq_len, head_dim))?
@ -345,7 +345,7 @@ impl Llama {
}
pub fn forward(&self, x: &Tensor, index_pos: usize) -> Result<Tensor> {
let (_b_sz, seq_len) = x.shape().r2()?;
let (_b_sz, seq_len) = x.dims2()?;
let mut x = self.wte.forward(x)?;
for (block_idx, block) in self.blocks.iter().enumerate() {
x = block.forward(&x, index_pos, block_idx)?;

8
candle-examples/examples/musicgen/musicgen_model.rs
View File

@ -123,7 +123,7 @@ impl MusicgenSinusoidalPositionalEmbedding {
}
fn forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
let (_b_sz, _codebooks, seq_len) = input_ids.shape().r3()?;
let (_b_sz, _codebooks, seq_len) = input_ids.dims3()?;
if seq_len > self.weights.dim(0)? {
self.weights = get_embedding(seq_len, self.embedding_dim)?
}
@ -170,7 +170,7 @@ impl MusicgenAttention {
kv_states: Option<&Tensor>,
attention_mask: &Tensor,
) -> Result<Tensor> {
let (b_sz, tgt_len, _) = xs.shape().r3()?;
let (b_sz, tgt_len, _) = xs.dims3()?;
let query_states = (self.q_proj.forward(xs)? * self.scaling)?;
let kv_states = kv_states.unwrap_or(xs);
@ -308,7 +308,7 @@ impl MusicgenDecoder {
fn forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
let dev = input_ids.device();
let (b_sz_times_codebooks, seq_len) = input_ids.shape().r2()?;
let (b_sz_times_codebooks, seq_len) = input_ids.dims2()?;
let b_sz = b_sz_times_codebooks / self.num_codebooks;
let input = input_ids.reshape((b_sz, self.num_codebooks, seq_len))?;
let mut inputs_embeds = Tensor::zeros((b_sz, seq_len, self.d_model), DType::F32, dev)?;
@ -352,7 +352,7 @@ impl MusicgenForCausalLM {
}
pub fn forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
let (b_sz, seq_len) = input_ids.shape().r2()?;
let (b_sz, seq_len) = input_ids.dims2()?;
let hidden_states = self.decoder.forward(input_ids)?;
let lm_logits = self
.lm_heads

2
candle-examples/examples/musicgen/t5_model.rs
View File

@ -338,7 +338,7 @@ impl T5Stack {
fn forward(&self, input_ids: &Tensor) -> Result<Tensor> {
let input_embeds = self.shared.as_ref().forward(input_ids)?;
let (_b_sz, _seq_len) = input_embeds.shape().r2()?;
let (_b_sz, _seq_len) = input_embeds.dims2()?;
let mut hidden_states = self.dropout.forward(&input_embeds)?;
for block in self.block.iter() {

2
candle-examples/examples/simple-training/main.rs
View File

@ -52,7 +52,7 @@ pub fn main() -> Result<()> {
.to_dtype(DType::F32)?
.sum_all()?
.to_scalar::<f32>()?;
let test_accuracy = sum_ok / test_labels.shape().r1()? as f32;
let test_accuracy = sum_ok / test_labels.dims1()? as f32;
println!(
"{epoch:4} train loss: {:8.5} test acc: {:5.2}%",
loss.to_scalar::<f32>()?,

4
candle-examples/examples/whisper/main.rs
View File

@ -127,7 +127,7 @@ impl Decoder {
.to_scalar::<f32>()? as f64;
}
let (seq_len, _) = logits.shape().r2()?;
let (seq_len, _) = logits.dims2()?;
let logits = logits
.get(seq_len - 1)?
.broadcast_add(&self.suppress_tokens)?;
@ -195,7 +195,7 @@ impl Decoder {
}
fn run(&mut self, mel: &Tensor) -> Result<Vec<Segment>> {
let (_, _, content_frames) = mel.shape().r3()?;
let (_, _, content_frames) = mel.dims3()?;
let mut seek = 0;
let mut segments = vec![];
while seek < content_frames {

6
candle-examples/examples/whisper/model.rs
View File

@ -132,7 +132,7 @@ impl MultiHeadAttention {
}
fn reshape_head(&self, x: &Tensor) -> Result<Tensor> {
let (n_batch, n_ctx, n_state) = x.shape().r3()?;
let (n_batch, n_ctx, n_state) = x.dims3()?;
let target_dims = &[n_batch, n_ctx, self.n_head, n_state / self.n_head];
Ok(x.reshape(target_dims)?.transpose(1, 2)?)
}
@ -144,7 +144,7 @@ impl MultiHeadAttention {
v: &Tensor,
mask: Option<&Tensor>,
) -> Result<Tensor> {
let (_, n_ctx, n_state) = q.shape().r3()?;
let (_, n_ctx, n_state) = q.dims3()?;
let scale = ((n_state / self.n_head) as f64).powf(-0.25);
let q = (self.reshape_head(q)? * scale)?;
let k = (self.reshape_head(k)?.transpose(2, 3)? * scale)?;
@ -270,7 +270,7 @@ impl AudioEncoder {
let x = self.conv1.forward(x)?.gelu()?;
let x = self.conv2.forward(&x)?.gelu()?;
let x = x.transpose(1, 2)?;
let (_bsize, seq_len, _hidden) = x.shape().r3()?;
let (_bsize, seq_len, _hidden) = x.dims3()?;
let positional_embedding = self.positional_embedding.narrow(0, 0, seq_len)?;
let mut x = x.broadcast_add(&positional_embedding)?;
for block in self.blocks.iter() {

2
candle-nn/src/conv.rs
View File

@ -41,7 +41,7 @@ impl Conv1d {
match &self.bias {
None => Ok(x),
Some(bias) => {
let b = bias.shape().r1()?;
let b = bias.dims1()?;
let bias = bias.reshape((1, b, 1))?;
Ok(x.broadcast_add(&bias)?)
}

2
candle-nn/src/layer_norm.rs
View File

@ -49,7 +49,7 @@ impl LayerNorm {
DType::F16 | DType::BF16 => DType::F32,
d => d,
};
let (_bsize, _seq_len, hidden_size) = x.shape().r3()?;
let (_bsize, _seq_len, hidden_size) = x.dims3()?;
let x = x.to_dtype(internal_dtype)?;
let mean_x = (x.sum_keepdim(2)? / hidden_size as f64)?;
let x = x.broadcast_sub(&mean_x)?;

6
candle-wasm-example/src/model.rs
View File

@ -164,7 +164,7 @@ impl MultiHeadAttention {
}
fn reshape_head(&self, x: &Tensor) -> Result<Tensor> {
let (n_batch, n_ctx, n_state) = x.shape().r3()?;
let (n_batch, n_ctx, n_state) = x.dims3()?;
let target_dims = &[n_batch, n_ctx, self.n_head, n_state / self.n_head];
Ok(x.reshape(target_dims)?.transpose(1, 2)?)
}
@ -176,7 +176,7 @@ impl MultiHeadAttention {
v: &Tensor,
mask: Option<&Tensor>,
) -> Result<Tensor> {
let (_, n_ctx, n_state) = q.shape().r3()?;
let (_, n_ctx, n_state) = q.dims3()?;
let scale = ((n_state / self.n_head) as f64).powf(-0.25);
let q = {
let _timer = crate::Timer::new("q::reshape");
@ -328,7 +328,7 @@ impl AudioEncoder {
self.conv2.forward(&x)?.gelu()?
};
let x = x.transpose(1, 2)?;
let (_bsize, seq_len, _hidden) = x.shape().r3()?;
let (_bsize, seq_len, _hidden) = x.dims3()?;
let positional_embedding = self.positional_embedding.narrow(0, 0, seq_len)?;
let mut x = x.broadcast_add(&positional_embedding)?;
for block in self.blocks.iter() {

4
candle-wasm-example/src/worker.rs
View File

@ -134,7 +134,7 @@ impl Decoder {
.to_scalar::<f32>()? as f64;
}
let (seq_len, _) = logits.shape().r2()?;
let (seq_len, _) = logits.dims2()?;
let logits = logits
.get(seq_len - 1)?
.broadcast_add(&self.suppress_tokens)?;
@ -207,7 +207,7 @@ impl Decoder {
fn run(&self, mel: &Tensor) -> anyhow::Result<Vec<Segment>> {
let mut rng = StdRng::seed_from_u64(299792458);
let (_, _, content_frames) = mel.shape().r3()?;
let (_, _, content_frames) = mel.dims3()?;
let mut seek = 0;
let mut segments = vec![];
while seek < content_frames {