add speculative decoding example for llama (#149)

* speculative decoding

* add sample 0

* spec decode gives same results as regular decode

* rebase

* use accept reject criteria

* switch to t5

* update readme

* readme nit

* nits

* nits

* nits

---------

Co-authored-by: Benjamin Anderson <benjamin@Benjamins-MBP.lan>
Co-authored-by: Awni Hannun <awni@apple.com>

llms/speculative_decoding/README.md

@@ -0,0 +1,66 @@
+# Speculative Decoding
+
+This example implements speculative decoding with the T5 model for text
+generation.[^1][^2] Speculative decoding uses a smaller draft model to propose
+several tokens, and a larger model to decide which tokens to accept. The
+distribution of the generated text is identical to what the larger model would
+produce on its own, but with far fewer forward passes of the large model since
+it can evaluate the draft tokens in parallel.
+
+### Setup
+
+First, install the requirements:
+
+```
+cd speculative_decoding
+pip install -r requirements.txt
+```
+
+Then convert the model and the draft model. We'll use T5-XXL (11B parameters)
+for the main model. Convert it with:
+
+```
+python convert.py --model t5-11b
+```
+
+We'll use T5-small for the draft model. Convert it with:
+
+```
+python convert.py --model t5-small
+```
+
+### Run
+
+You can run with the default arguments:
+
+```
+python main.py
+```
+
+To see a full list of options use:
+```
+python main.py --help
+```
+
+### Notes
+
+Speculative decoding works well when most of the tokens from the draft model
+are accepted by the larger model. That's more likely to happen if the models
+are trained on similar data.
+
+One way to increase the chance of accepting a draft token is with the parameter
+`--delta`. This parameter can be in the range $[0, 1]$. If it is $1$ then all
+the draft tokens will be accepted by the model. If it is $0$, then only draft
+tokens which match the original acceptance criterion are kept.[^1] Values
+closer to $1$ increase the chance that a draft token is accepted.
+
+Conversely, the fewer draft tokens accepted by the main model, the more
+expensive speculative decoding is. You can use `--num-draft` to tune the number
+of draft tokens per model evaluation in order to reduce the number of discarded
+draft tokens. Decreasing `--num-draft` will decrease the number of discarded
+draft tokens at the expense of more large model evaluations.
+
+[^1]: See the paper [Fast Inference from Transformers via Speculative
+Decoding](https://arxiv.org/abs/2211.17192)
+[^2]: For more information on T5 see the [original paper](https://arxiv.org/abs/1910.10683)
+   or the [Hugging Face page](https://huggingface.co/docs/transformers/model_doc/t5).

llms/speculative_decoding/convert.py

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+import numpy as np
+from transformers import T5ForConditionalGeneration
+
+SHARED_REPLACEMENT_PATTERNS = [
+    (".block.", ".layers."),
+    (".k.", ".key_proj."),
+    (".o.", ".out_proj."),
+    (".q.", ".query_proj."),
+    (".v.", ".value_proj."),
+    ("shared.", "wte."),
+    ("lm_head.", "lm_head.linear."),
+    (".layer.0.layer_norm.", ".ln1."),
+    (".layer.1.layer_norm.", ".ln2."),
+    (".layer.2.layer_norm.", ".ln3."),
+    (".final_layer_norm.", ".ln."),
+    (
+        "layers.0.layer.0.SelfAttention.relative_attention_bias.",
+        "relative_attention_bias.embeddings.",
+    ),
+]
+
+ENCODER_REPLACEMENT_PATTERNS = [
+    (".layer.0.SelfAttention.", ".attention."),
+    (".layer.1.DenseReluDense.", ".dense."),
+]
+
+DECODER_REPLACEMENT_PATTERNS = [
+    (".layer.0.SelfAttention.", ".self_attention."),
+    (".layer.1.EncDecAttention.", ".cross_attention."),
+    (".layer.2.DenseReluDense.", ".dense."),
+]
+
+
+def replace_key(key: str) -> str:
+    for old, new in SHARED_REPLACEMENT_PATTERNS:
+        key = key.replace(old, new)
+    if key.startswith("encoder."):
+        for old, new in ENCODER_REPLACEMENT_PATTERNS:
+            key = key.replace(old, new)
+    elif key.startswith("decoder."):
+        for old, new in DECODER_REPLACEMENT_PATTERNS:
+            key = key.replace(old, new)
+    return key
+
+
+def convert(model_name, dtype):
+    dtype = getattr(np, dtype)
+    model = T5ForConditionalGeneration.from_pretrained(model_name, torch_dtype="auto")
+    weights = {
+        replace_key(k): v.numpy().astype(dtype) for k, v in model.state_dict().items()
+    }
+    file_name = model_name.replace("/", "-")
+    print(f"Saving weights to {file_name}.npz")
+    np.savez(f"{file_name}.npz", **weights)
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Convert T5 weights to MLX")
+    parser.add_argument(
+        "--model",
+        type=str,
+        help="Name of the T5 model.",
+        default="t5-small",
+    )
+    parser.add_argument(
+        "--dtype",
+        help="The model data type.",
+        type=str,
+        choices=["float16", "float32"],
+        default="float32",
+    )
+    args = parser.parse_args()
+    convert(args.model, args.dtype)

llms/speculative_decoding/decoder.py

@@ -0,0 +1,191 @@
+from dataclasses import dataclass, field
+from typing import List, Optional
+
+import mlx.core as mx
+import mlx.nn as nn
+import numpy as np
+import transformers
+from model import Model
+
+
+class Tokenizer:
+    def __init__(self, model_name: str):
+        self._tokenizer = transformers.AutoTokenizer.from_pretrained(
+            model_name,
+            legacy=False,
+            model_max_length=512,
+        )
+        self._decoder_start_id = 0
+
+    @property
+    def eos_id(self) -> int:
+        return self._tokenizer.eos_token_id
+
+    @property
+    def decoder_start_id(self) -> int:
+        return self._decoder_start_id
+
+    def encode(self, s: str) -> mx.array:
+        return mx.array(
+            self._tokenizer(s, return_tensors="np", return_attention_mask=False,)[
+                "input_ids"
+            ].squeeze(0)
+        )
+
+    def decode(self, t: List[int]) -> str:
+        return self._tokenizer.decode(t)
+
+
+class SpeculativeDecoder:
+    def __init__(
+        self,
+        model: Model,
+        draft_model: Model,
+        tokenizer: str,
+        num_draft: int = 5,
+        delta: float = 0.0,
+    ):
+        self.tokenizer = Tokenizer(tokenizer)
+        self.model = model
+        self.draft_model = draft_model
+        self.num_draft = num_draft
+        self.delta = delta
+
+    def _generate(
+        self,
+        x: mx.array,
+        memory: mx.array,
+        draft: bool = False,
+    ):
+        model = self.draft_model if draft else self.model
+        while True:
+            logits = model.decode(x[None], memory)[0, -1]
+            x = mx.argmax(logits, keepdims=True)
+            lognorm = mx.logsumexp(logits.astype(mx.float32))
+            logprob = logits[x] - lognorm
+            yield x, logprob
+
+    def generate(
+        self,
+        prompt,
+        max_tokens: int = 100,
+    ):
+        memory = self.model.encode(self.tokenizer.encode(prompt)[None])
+        x = mx.array([self.tokenizer.decoder_start_id])
+        skip = 0
+        outputs = []
+        for (token, _), n in zip(self._generate(x, memory), range(max_tokens)):
+            if token == self.tokenizer.eos_id:
+                break
+            outputs.append(token.item())
+            if (n + 1) % 10 == 0:
+                str_output = self.tokenizer.decode(outputs)
+                print(str_output[skip:], end="", flush=True)
+                skip = len(str_output)
+
+        print(self.tokenizer.decode(outputs)[skip:], end="", flush=True)
+        print()
+        self.model.reset_cache()
+
+    def _get_num_accept(self, draft_tokens, draft_probs, model_logits):
+        # accept_toks = mx.argmax(model_logits, axis=-1) == draft_tokens
+        model_probs = mx.take_along_axis(
+            model_logits,
+            draft_tokens[:, None],
+            axis=-1,
+        ).squeeze(-1)
+        model_probs -= mx.logsumexp(model_logits.astype(mx.float32), axis=-1)
+        unis = mx.random.uniform(shape=(draft_tokens.size,))
+        log_unis = mx.log(mx.maximum(unis - self.delta, 0.0))
+        accept_toks = log_unis <= ((model_probs - draft_probs))
+        num_to_accept = (accept_toks.tolist() + [False]).index(False)
+        return num_to_accept
+
+    def speculative_decode(
+        self,
+        prompt,
+        max_tokens: int = 100,
+    ):
+        def sample(logits):
+            return mx.argmax(logits, axis=-1)
+
+        prompt = mx.array(self.tokenizer.encode(prompt), mx.uint32)[None]
+        memory = self.model.encode(prompt)
+        draft_memory = self.draft_model.encode(prompt)
+
+        tokens = mx.array([self.tokenizer.decoder_start_id])
+
+        n_steps = 0
+        ntoks = 0
+        n_accepted = 0
+        n_draft = 0
+
+        outputs = []
+        skip = 0
+        draft_inputs = tokens
+        inputs = tokens
+        while True:
+            # For each decoding step: generate n tokens from a draft model
+            draft_tokens = []
+            draft_probs = []
+            for _, (t, p) in zip(
+                range(ntoks, min(ntoks + self.num_draft, max_tokens)),
+                self._generate(draft_inputs, draft_memory, draft=True),
+            ):
+                draft_tokens.append(t)
+                draft_probs.append(p)
+                if t.item() == self.tokenizer.eos_id:
+                    break
+
+            # Verify the draft tokens with the last verified token:
+            draft_tokens = mx.concatenate(draft_tokens)
+            draft_probs = mx.concatenate(draft_probs)
+            verify_tokens = mx.concatenate([inputs, draft_tokens])
+            logits = self.model.decode(
+                verify_tokens[None, :],
+                memory,
+            ).squeeze(0)
+
+            # Only keep samples that match the draft:
+            num_to_accept = self._get_num_accept(
+                draft_tokens,
+                draft_probs,
+                logits[:-1],
+            )
+            new_tokens = draft_tokens[:num_to_accept]
+            # Get the next token from the main model as well
+            new_tokens = mx.concatenate(
+                [new_tokens, mx.argmax(logits[num_to_accept], keepdims=True)]
+            )
+
+            n_accepted += num_to_accept
+            n_draft += draft_tokens.size
+
+            # Rewind the cache for unaccepted tokens:
+            if (n := draft_tokens.size) > num_to_accept:
+                self.draft_model.truncate_cache(n - new_tokens.size)
+                self.model.truncate_cache(n - new_tokens.size + 1)
+
+            n_steps += 1
+
+            for t in new_tokens.tolist():
+                if t == self.tokenizer.eos_id or ntoks >= max_tokens:
+                    break
+                outputs.append(t)
+                ntoks += 1
+
+            str_output = self.tokenizer.decode(outputs)
+            print(str_output[skip:], end="", flush=True)
+            skip = len(str_output)
+
+            if ntoks >= max_tokens or new_tokens[-1] == self.tokenizer.eos_id:
+                break
+            draft_inputs = new_tokens[max(new_tokens.size - 2, 0) :]
+            inputs = draft_inputs[-1:]
+
+        print(self.tokenizer.decode(outputs)[skip:], end="", flush=True)
+        print()
+
+        self.model.reset_cache()
+        self.draft_model.reset_cache()
+        return {"n_accepted": n_accepted, "n_draft": n_draft, "n_steps": n_steps}

llms/speculative_decoding/main.py

@@ -0,0 +1,99 @@
+import argparse
+import glob
+import json
+import time
+from pathlib import Path
+
+import mlx.core as mx
+import mlx.nn as nn
+from decoder import SpeculativeDecoder
+from mlx.utils import tree_unflatten
+from model import Model
+from transformers import T5Config
+
+
+def load_model(model_name: str):
+    config = T5Config.from_pretrained(model_name)
+    model = Model(config)
+    weights = mx.load(f"{model_name}.npz")
+    weights = tree_unflatten(list(weights.items()))
+    model.update(weights)
+    mx.eval(model.parameters())
+    return model
+
+
+def main(args):
+    mx.random.seed(args.seed)
+
+    spec_decoder = SpeculativeDecoder(
+        model=load_model(args.model_name),
+        draft_model=load_model(args.draft_model_name),
+        tokenizer=args.model_name,
+        delta=args.delta,
+        num_draft=args.num_draft,
+    )
+
+    tic = time.time()
+    print(args.prompt)
+    if args.regular_decode:
+        spec_decoder.generate(args.prompt, max_tokens=args.max_tokens)
+    else:
+        stats = spec_decoder.speculative_decode(args.prompt, max_tokens=args.max_tokens)
+        print("=" * 10)
+        print(f"Accepted {stats['n_accepted']} / {stats['n_draft']}.")
+        print(f"Decoding steps {stats['n_steps']}.")
+
+    toc = time.time()
+    print("=" * 10)
+    print(f"Full generation time {toc - tic:.3f}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Convert Llama weights to MLX")
+    parser.add_argument(
+        "--num-draft",
+        type=int,
+        default=5,
+        help="Number of draft tokens to use per decoding step.",
+    )
+    parser.add_argument(
+        "--model-name",
+        help="Name of the model.",
+        default="t5-small",
+    )
+    parser.add_argument(
+        "--draft-model-name",
+        help="Name of the draft model.",
+        default="t5-small",
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=0,
+        help="PRNG seed.",
+    )
+    parser.add_argument(
+        "--max-tokens",
+        "-m",
+        type=int,
+        default=100,
+        help="Maximum number of tokens to generate.",
+    )
+    parser.add_argument(
+        "--prompt",
+        default="translate English to French: Let's go to the store and buy some groceries including eggs, avocadoes, and bread.",
+        help="The prompt processed by the model.",
+    )
+    parser.add_argument(
+        "--delta",
+        type=float,
+        default=0.1,
+        help="Lenience for accepting the proposal tokens.",
+    )
+    parser.add_argument(
+        "--regular-decode",
+        action="store_true",
+        help="Use regular decoding instead of speculative decoding.",
+    )
+    args = parser.parse_args()
+    main(args)

llms/speculative_decoding/model.py

@@ -0,0 +1,341 @@
+from typing import List, Optional, Tuple
+
+import mlx.core as mx
+import mlx.nn as nn
+import numpy as np
+from mlx.utils import tree_map, tree_unflatten
+from transformers import AutoTokenizer, T5Config
+
+
+def _relative_position_bucket(
+    relative_position, bidirectional=True, num_buckets=32, max_distance=128
+):
+    """
+    Adapted from HF Tensorflow:
+    https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py
+
+    Translate relative position to a bucket number for relative attention. The relative position is defined as
+    memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
+    position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
+    small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
+    positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
+    This should allow for more graceful generalization to longer sequences than the model has been trained on
+
+    Args:
+        relative_position: an int32 Tensor
+        bidirectional: a boolean - whether the attention is bidirectional
+        num_buckets: an integer
+        max_distance: an integer
+
+    Returns:
+        a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
+    """
+    relative_buckets = 0
+    if bidirectional:
+        num_buckets //= 2
+        relative_buckets += (relative_position > 0).astype(mx.int16) * num_buckets
+        relative_position = mx.abs(relative_position)
+    else:
+        relative_position = -mx.minimum(
+            relative_position, mx.zeros_like(relative_position)
+        )
+    # now relative_position is in the range [0, inf)
+
+    # half of the buckets are for exact increments in positions
+    max_exact = num_buckets // 2
+    is_small = relative_position < max_exact
+
+    # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+    scale = (num_buckets - max_exact) / np.log(max_distance / max_exact)
+    relative_position_if_large = max_exact + (
+        mx.log(relative_position.astype(mx.float32) / max_exact) * scale
+    ).astype(mx.int16)
+    relative_position_if_large = mx.minimum(relative_position_if_large, num_buckets - 1)
+    relative_buckets += mx.where(
+        is_small, relative_position, relative_position_if_large
+    )
+    return relative_buckets
+
+
+class RelativePositionBias(nn.Module):
+    def __init__(self, config: T5Config, bidirectional: bool):
+        self.bidirectional = bidirectional
+        self.num_buckets = config.relative_attention_num_buckets
+        self.max_distance = config.relative_attention_max_distance
+        self.n_heads = config.num_heads
+        self.embeddings = nn.Embedding(
+            config.relative_attention_num_buckets, config.num_heads
+        )
+
+    def __call__(self, query_length: int, key_length: int, offset: int = 0):
+        """Compute binned relative position bias"""
+        context_position = mx.arange(offset, query_length)[:, None]
+        memory_position = mx.arange(key_length)[None, :]
+
+        # shape (query_length, key_length)
+        relative_position = memory_position - context_position
+        relative_position_bucket = _relative_position_bucket(
+            relative_position,
+            bidirectional=self.bidirectional,
+            num_buckets=self.num_buckets,
+            max_distance=self.max_distance,
+        )
+
+        # shape (query_length, key_length, num_heads)
+        values = self.embeddings(relative_position_bucket)
+
+        # shape (num_heads, query_length, key_length)
+        return values.transpose(2, 0, 1)
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, config: T5Config):
+        super().__init__()
+        inner_dim = config.d_kv * config.num_heads
+        self.num_heads = config.num_heads
+        self.query_proj = nn.Linear(config.d_model, inner_dim, bias=False)
+        self.key_proj = nn.Linear(config.d_model, inner_dim, bias=False)
+        self.value_proj = nn.Linear(config.d_model, inner_dim, bias=False)
+        self.out_proj = nn.Linear(inner_dim, config.d_model, bias=False)
+
+    def __call__(
+        self,
+        queries: mx.array,
+        keys: mx.array,
+        values: mx.array,
+        mask: Optional[mx.array],
+        cache: Optional[Tuple[mx.array, mx.array]] = None,
+    ) -> [mx.array, Tuple[mx.array, mx.array]]:
+        queries = self.query_proj(queries)
+        keys = self.key_proj(keys)
+        values = self.value_proj(values)
+
+        num_heads = self.num_heads
+        B, L, _ = queries.shape
+        _, S, _ = keys.shape
+        queries = queries.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
+        keys = keys.reshape(B, S, num_heads, -1).transpose(0, 2, 1, 3)
+        values = values.reshape(B, S, num_heads, -1).transpose(0, 2, 1, 3)
+
+        if cache is not None:
+            key_cache, value_cache = cache
+            keys = mx.concatenate([key_cache, keys], axis=2)
+            values = mx.concatenate([value_cache, values], axis=2)
+
+        # Dimensions are [batch x num heads x sequence x hidden dim]
+        scores = queries @ keys.transpose(0, 1, 3, 2)
+        if mask is not None:
+            scores = scores + mask.astype(scores.dtype)
+
+        scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
+        values_hat = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
+        return self.out_proj(values_hat), (keys, values)
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dims: int, eps: float = 1e-5):
+        super().__init__()
+        self.weight = mx.ones((dims,))
+        self.eps = eps
+
+    def _norm(self, x):
+        return x * mx.rsqrt(x.square().mean(-1, keepdims=True) + self.eps)
+
+    def __call__(self, x):
+        t = x.dtype
+        output = self._norm(x).astype(t)
+        return self.weight * output
+
+
+class DenseActivation(nn.Module):
+    def __init__(self, config: T5Config):
+        super().__init__()
+        mlp_dims = config.d_ff or config.d_model * 4
+        self.gated = config.feed_forward_proj.startswith("gated")
+        if self.gated:
+            self.wi_0 = nn.Linear(config.d_model, mlp_dims, bias=False)
+            self.wi_1 = nn.Linear(config.d_model, mlp_dims, bias=False)
+        else:
+            self.wi = nn.Linear(config.d_model, mlp_dims, bias=False)
+        self.wo = nn.Linear(mlp_dims, config.d_model, bias=False)
+        activation = config.feed_forward_proj.removeprefix("gated-")
+        if activation == "relu":
+            self.act = nn.relu
+        elif activation == "gelu":
+            self.act = nn.gelu
+        elif activation == "silu":
+            self.act = nn.silu
+        else:
+            raise ValueError(f"Unknown activation: {activation}")
+
+    def __call__(self, x):
+        if self.gated:
+            hidden_act = self.act(self.wi_0(x))
+            hidden_linear = self.wi_1(x)
+            x = hidden_act * hidden_linear
+        else:
+            x = self.act(self.wi(x))
+        return self.wo(x)
+
+
+class TransformerEncoderLayer(nn.Module):
+    def __init__(self, config: T5Config):
+        super().__init__()
+        self.attention = MultiHeadAttention(config)
+        self.ln1 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.ln2 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.dense = DenseActivation(config)
+
+    def __call__(self, x, mask):
+        y = self.ln1(x)
+        y, _ = self.attention(y, y, y, mask=mask)
+        x = x + y
+
+        y = self.ln2(x)
+        y = self.dense(y)
+        return x + y
+
+
+class TransformerEncoder(nn.Module):
+    def __init__(self, config: T5Config):
+        super().__init__()
+        self.layers = [
+            TransformerEncoderLayer(config) for i in range(config.num_layers)
+        ]
+        self.ln = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.relative_attention_bias = RelativePositionBias(config, bidirectional=True)
+
+    def __call__(self, x: mx.array):
+        pos_bias = self.relative_attention_bias(x.shape[1], x.shape[1])
+        for layer in self.layers:
+            x = layer(x, mask=pos_bias)
+        return self.ln(x)
+
+
+class TransformerDecoderLayer(nn.Module):
+    def __init__(self, config: T5Config):
+        super().__init__()
+        self.self_attention = MultiHeadAttention(config)
+        self.cross_attention = MultiHeadAttention(config)
+        self.ln1 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.ln2 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.ln3 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.dense = DenseActivation(config)
+
+    def __call__(
+        self,
+        x: mx.array,
+        memory: mx.array,
+        mask: mx.array,
+        memory_mask: mx.array,
+        cache: Optional[List[Tuple[mx.array, mx.array]]] = None,
+    ):
+        y = self.ln1(x)
+        y, cache = self.self_attention(y, y, y, mask, cache)
+        x = x + y
+
+        y = self.ln2(x)
+        y, _ = self.cross_attention(y, memory, memory, memory_mask)
+        x = x + y
+
+        y = self.ln3(x)
+        y = self.dense(y)
+        x = x + y
+
+        return x, cache
+
+
+def create_additive_causal_mask(N: int, offset: int = 0):
+    rinds = mx.arange(offset + N)
+    linds = mx.arange(offset, offset + N) if offset else rinds
+    mask = linds[:, None] < rinds[None]
+    return mask * -1e9
+
+
+class TransformerDecoder(nn.Module):
+    def __init__(self, config: T5Config):
+        super().__init__()
+        n_layers = getattr(config, "num_decoder_layers", config.num_layers)
+        self.layers = [TransformerDecoderLayer(config) for i in range(n_layers)]
+        self.ln = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.relative_attention_bias = RelativePositionBias(config, bidirectional=False)
+
+    def __call__(self, x, memory, cache=None):
+        if cache[0] is not None:
+            offset = cache[0][0].shape[2]
+        else:
+            offset = 0
+
+        T = x.shape[1]
+        if T > 1:
+            mask = create_additive_causal_mask(T, offset)
+        else:
+            mask = None
+
+        pos_bias = self.relative_attention_bias(T + offset, T + offset, offset=offset)
+        if mask is not None:
+            mask += pos_bias
+        else:
+            mask = pos_bias
+
+        for e, layer in enumerate(self.layers):
+            x, cache[e] = layer(x, memory, mask, None, cache=cache[e])
+        x = self.ln(x)
+
+        return x, cache
+
+
+class OutputHead(nn.Module):
+    def __init__(self, config: T5Config):
+        self.linear = nn.Linear(config.d_model, config.vocab_size, bias=False)
+
+    def __call__(self, inputs):
+        return self.linear(inputs)
+
+
+class Model(nn.Module):
+    def __init__(self, config: T5Config):
+        self.wte = nn.Embedding(config.vocab_size, config.d_model)
+        self.encoder = TransformerEncoder(config)
+        self.decoder = TransformerDecoder(config)
+        self.tie_word_embeddings = config.tie_word_embeddings
+        if not self.tie_word_embeddings:
+            self.lm_head = OutputHead(config)
+        self.model_dim = config.d_model
+        self.reset_cache()
+
+    def encode(self, inputs: mx.array):
+        return self.encoder(self.wte(inputs))
+
+    def truncate_cache(self, num_to_truncate):
+        if num_to_truncate <= 0:
+            return
+        cache_length = self.cache[0][0].shape[2]
+        if num_to_truncate < cache_length:
+            self.cache = tree_map(lambda x: x[:, :, :-num_to_truncate, :], self.cache)
+        else:
+            self.reset_cache()
+
+    def reset_cache(self):
+        self.cache = [None] * len(self.decoder.layers)
+
+    def decode(
+        self,
+        inputs: mx.array,
+        memory: mx.array,
+    ):
+        inputs = self.wte(inputs)
+        y, self.cache = self.decoder(inputs, memory=memory, cache=self.cache)
+        if not self.tie_word_embeddings:
+            y *= self.model_dim**-0.5
+            y = self.lm_head(y)
+        else:
+            y = y @ self.wte.weight.T
+        return y
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        decoder_inputs: mx.array,
+    ):
+        return self.decode(decoder_inputs, self.encode(inputs))[0]

llms/speculative_decoding/requirements.txt

@@ -0,0 +1,3 @@
+mlx>=0.0.6
+transformers
+numpy

t5/t5.py

@@ -125,7 +125,6 @@ class MultiHeadAttention(nn.Module):
             values = mx.concatenate([value_cache, values], axis=2)
 
         # Dimensions are [batch x num heads x sequence x hidden dim]
-        queries = queries
         scores = queries @ keys
         if mask is not None:
             scores = scores + mask.astype(scores.dtype)