bump

gradio_app.py

@@ -1,86 +1,36 @@
 import gradio as gr
 import subprocess
 import sys
-from pathlib import Path
+import os
 
 
-async def generate(prompt, model_name, seed=0, temperature=0.5, num_tokens=256):
+async def generate(prompt):
+    # os.environ["PROMPT"] = prompt
     # stream stout
     process = subprocess.Popen(
-        [
-            "mojo",
-            "llama2.mojo",
-            Path(model_name),
-            "-s",
-            str(seed),
-            "-n",
-            str(num_tokens),
-            "-t",
-            str(temperature),
-            "-i",
-            prompt,
-        ],
-        stdout=subprocess.PIPE,
-        stderr=subprocess.PIPE,
+        ["mojo", "llama2.mojo"], stdout=subprocess.PIPE, stderr=subprocess.PIPE
     )
     text = ""
     for char in iter(lambda: process.stdout.read(1), b""):
-        char_decoded = char.decode("utf-8", errors="ignore")
+        char_decoded = char.decode()
+        sys.stdout.write(char_decoded)
         text += char_decoded
         yield text
 
 
-with gr.Blocks() as demo:
-    gr.Markdown(
-        """
+output_text = gr.Textbox(label="Generated Text")
+
+demo = gr.Interface(
+    fn=generate,
+    inputs=None,
+    outputs=output_text,
+    description="""
 # llama2.🔥
 ## [Mojo](https://docs.modular.com/mojo/) implementation of [llama2.c](https://github.com/karpathy/llama2.c) by [@tairov](https://github.com/tairov)
 Source: https://github.com/tairov/llama2.mojo
-    """
-    )
-    with gr.Row():
-        with gr.Column():
-            prompt = gr.Textbox(label="Prompt", placeholder="Add your prompt here...")
-            seed = gr.Slider(
-                minimum=0,
-                maximum=2**53,
-                value=0,
-                step=1,
-                label="Seed",
-                randomize=True,
-            )
-            temperature = gr.Slider(
-                minimum=0.0, maximum=2.0, step=0.01, value=0.5, label="Temperature"
-            )
-            num_tokens = gr.Slider(
-                minimum=1, maximum=256, value=256, label="Number of tokens"
-            )
-            model_name = gr.Dropdown(
-                ["stories15M.bin", "stories42M.bin", "stories110M.bin"],
-                value="stories15M.bin",
-                label="Model Size",
-            )
-            with gr.Row():
-                stop = gr.Button("Stop")
-                run = gr.Button("Run")
-        with gr.Column(scale=2):
-            output_text = gr.Textbox(label="Generated Text")
-
-    # update maximum number of tokens based on model size
-    model_name.change(
-        lambda x: gr.update(maximum=1024)
-        if x == "stories110M.bin" or x == "stories42M.bin"
-        else gr.update(maximum=256),
-        model_name,
-        num_tokens,
-        queue=False,
-    )
-    click_event = run.click(
-        fn=generate,
-        inputs=[prompt, model_name, seed, temperature, num_tokens],
-        outputs=output_text,
-    )
-    stop.click(fn=None, inputs=None, outputs=None, cancels=[click_event])
+    """,
+    allow_flagging="never",
+)
 
 demo.queue()
 demo.launch(server_name="0.0.0.0")

llama2.mojo

@@ -1,22 +1,25 @@
-from algorithm import sum
-from algorithm import vectorize, parallelize
-from builtin import string
 from math import round
+import math
+
 from memory import memset_zero, memcpy
-from memory.buffer import Buffer
 from memory.unsafe import DTypePointer
-from python import Python
 from random import rand
+from sys.info import simdwidthof
+from builtin import string
+import time
+import random
+import os
+
+from runtime.llcl import num_cores
+
 from read import BufReader, File
-from runtime.llcl import num_cores, Runtime
-from sys import argv
+from memory.buffer import Buffer
+
+from python import Python
 
 # The SIMD vector width.
-from sys.info import simdwidthof
-import math
-import os
-import random
-import time
+from algorithm import vectorize, parallelize
+from algorithm import sum
 
 alias nelts = (2 * simdwidthof[DType.float32]())
 
@@ -26,51 +29,98 @@ alias BufferPtrFloat32 = DTypePointer[DType.float32]
 alias PointerStrings = Pointer[PointerString]
 
 
-struct Matrix:
+struct Matrix3:
     var data: BufferPtrFloat32
     var rows: Int
     var cols: Int
     var layers: Int
     var allocated: Int
 
+    fn __init__(inout self, layers: Int, rows: Int, cols: Int):
+        self.data = BufferPtrFloat32.alloc(0)
+        self.rows = rows
+        self.cols = cols
+        self.layers = layers
+        self.allocated = 0
+
+    @always_inline
+    fn alloc(inout self, fill: Int = 0):
+        self.data = BufferPtrFloat32.alloc(self.size())
+        self.allocated = 1
+        if fill == 1:
+            self.zero()
+
+    @always_inline
+    fn alloc_zero(inout self):
+        self.alloc(1)
+
+    @always_inline
+    fn set_buf_ptr(inout self, ptr: BufferPtrFloat32):
+        self.data = ptr
+
+    fn __del__(owned self):
+        if self.allocated == 1:
+            self.data.free()
+
+    @always_inline
+    fn zero(inout self):
+        memset_zero(self.data, self.layers * self.rows * self.cols)
+
+    @always_inline
+    fn size(inout self) -> Int:
+        return self.layers * self.cols * self.rows
+
+    @always_inline
+    fn __getitem__(self, z: Int, y: Int, x: Int) -> Float32:
+        return self.load[1](z, y, x)
+
+    @always_inline
+    fn load[nelts: Int](self, z: Int, y: Int, x: Int) -> SIMD[DType.float32, nelts]:
+        return self.data.simd_load[nelts](z * self.layers + y * self.cols + x)
+
+    @always_inline
+    fn __setitem__(self, z: Int, y: Int, x: Int, val: Float32):
+        return self.store[1](z, y, x, val)
+
+    @always_inline
+    fn store[nelts: Int](self, z: Int, y: Int, x: Int, val: SIMD[DType.float32, nelts]):
+        self.data.simd_store[nelts](z * self.layers + y * self.cols + x, val)
+
+
+struct Matrix:
+    var data: BufferPtrFloat32
+    var rows: Int
+    var cols: Int
+    var allocated: Int
+
     fn __init__(inout self, rows: Int, cols: Int):
         self.data = BufferPtrFloat32.alloc(0)
         self.rows = rows
         self.cols = cols
-        self.layers = 1
         self.allocated = 0
 
     fn __init__(inout self, cols: Int):
         self.data = BufferPtrFloat32.alloc(0)
         self.rows = 1
-        self.layers = 1
         self.cols = cols
         self.allocated = 0
 
-    fn __init__(inout self, layers: Int, rows: Int, cols: Int):
-        self.__init__(rows, cols)
-        self.layers = layers
-
     fn __del__(owned self):
         if self.allocated == 1:
             self.data.free()
 
-    @always_inline
     fn alloc(inout self, fill: Int = 0):
         self.data = BufferPtrFloat32.alloc(self.size())
         self.allocated = 1
         if fill == 1:
             self.zero()
 
-    @always_inline
     fn alloc_zero(inout self):
         self.alloc(1)
 
-    @always_inline
     fn zero(inout self):
-        memset_zero(self.data, self.size())
+        memset_zero(self.data, self.rows * self.cols)
 
-    @always_inline
     fn set_buf_ptr(inout self, ptr: BufferPtrFloat32):
         self.data = ptr
 
@@ -80,9 +130,8 @@ struct Matrix:
         self.rows = rows
         self.cols = cols
 
-    @always_inline
     fn size(inout self) -> Int:
-        return self.cols * self.rows * self.layers
+        return self.cols * self.rows
 
     @always_inline
     fn __getitem__(self, y: Int, x: Int) -> Float32:
@@ -116,131 +165,32 @@ struct Matrix:
     fn store[nelts: Int](self, x: Int, val: SIMD[DType.float32, nelts]):
         self.data.simd_store[nelts](x, val)
 
-    @always_inline
-    fn __getitem__(self, z: Int, y: Int, x: Int) -> Float32:
-        return self.load[1](z, y, x)
-
-    @always_inline
-    fn load[nelts: Int](self, z: Int, y: Int, x: Int) -> SIMD[DType.float32, nelts]:
-        return self.data.simd_load[nelts](z * self.layers + y * self.cols + x)
-
-    @always_inline
-    fn __setitem__(self, z: Int, y: Int, x: Int, val: Float32):
-        return self.store[1](z, y, x, val)
-
-    @always_inline
-    fn store[nelts: Int](self, z: Int, y: Int, x: Int, val: SIMD[DType.float32, nelts]):
-        self.data.simd_store[nelts](z * self.layers + y * self.cols + x, val)
 
-
-fn read_val_int(inout buf: FileBuf) raises -> Int:
+fn read_val_int(inout buf: FileBuf) -> Int:
     # DTypePointer[DType.ui8](buf.data).bitcast[DType.ui8]()
-    let data = buf.data.offset(buf.get_offset()).bitcast[DType.uint32]()
-    let result = data.load(0)
-    buf.move_offset(4)
+    let data = buf.data.offset(buf.offset).bitcast[DType.uint32]()
+    let result = data.simd_load[1](0)
+    buf.offset += 4
     return result.to_int()
 
 
-fn read_val_float32(inout buf: FileBuf) raises -> Float32:
+fn read_val_float32(inout buf: FileBuf) -> Float32:
     # DTypePointer[DType.ui8](buf.data).bitcast[DType.ui8]()
-    let val = buf.data.offset(buf.get_offset()).bitcast[DType.float32]().load(0)
-    buf.move_offset(4)
+    let val = buf.data.offset(buf.offset).bitcast[DType.float32]().simd_load[1](0)
+    buf.offset += 4
     return val
 
 
-fn read_val_str(inout buf: FileBuf, slen: Int) raises -> PointerString:
-    
+fn read_val_str(inout buf: FileBuf, slen: Int) -> PointerString:
     let str = PointerString.alloc(slen + 1)
     for i in range(slen):
-        str.store(i, buf.data.load(buf.get_offset()))
-        buf.move_offset(1)
+        str.store(i, buf.data.simd_load[1](buf.offset))
+        buf.offset += 1
     str.store(slen, 0)
 
     return str
 
 
-# not optimal concat
-fn str_concat(s1: PointerString, s2: PointerString) -> PointerString:
-    var l1 = 0
-    var l2 = 0
-
-    while s1[l1] != 0:
-        l1 += 1
-    while s2[l2] != 0:
-        l2 += 1
-
-    let str = PointerString.alloc(l1 + l2 + 1)
-    memcpy[UInt8](str, s1, l1)
-    memcpy[UInt8](str.offset(l1), s2, l2)
-    str.store(l1 + l2, 0)
-    return str
-
-
-fn str_to_ptr(s: String) -> PointerString:
-    let ret = PointerString.alloc(len(s) + 1)
-    for i in range(len(s)):
-        ret.store(i, ord(s[i]))
-    ret.store(len(s), 0)
-    return ret
-
-
-fn string_compare(a: PointerString, b: PointerString) -> Int:
-    var index = 0
-    while a[index] != 0 and b[index] != 0:
-        if a[index] < b[index]:
-            return -1
-        if a[index] > b[index]:
-            return 1
-
-        index += 1
-
-    if a[index] != 0 and b[index] == 0:
-        return 1
-
-    if a[index] == 0 and b[index] != 0:
-        return -1
-
-    return 0
-
-
-# Quicksort helper function to find the partition position
-fn partition(
-    inout array: PointerStrings, inout indices: DynamicVector[Int], low: Int, high: Int
-) -> Int:
-    let pivot = array[high]
-    var ii = low - 1
-    for jj in range(low, high):
-        if string_compare(pivot, array[jj]) == 1:
-            # If element smaller than pivot, swap
-            ii = ii + 1
-
-            let tmp = array[ii]
-            let tmp_idx = indices[ii]
-            array.store(ii, array[jj])
-            indices[ii] = indices[jj]
-            array.store(jj, tmp)
-            indices[jj] = tmp_idx
-
-    # Swap the pivot element
-    let tmp = array[ii + 1]
-    let tmp_idx = indices[ii + 1]
-    array.store(ii + 1, array[high])
-    indices[ii + 1] = indices[high]
-    array.store(high, tmp)
-    indices[high] = tmp_idx
-
-    return ii + 1
-
-
-fn quicksort(
-    inout array: PointerStrings, inout indices: DynamicVector[Int], low: Int, high: Int
-):
-    if low < high:
-        let pi = partition(array, indices, low, high)
-        quicksort(array, indices, low, pi - 1)
-        quicksort(array, indices, pi + 1, high)
-
-
 struct FileBuf:
     var data: BufferPtrType
     var offset: Int
@@ -251,95 +201,36 @@ struct FileBuf:
         self.offset = 0
         self.size = 0
 
-    fn move_offset(inout self, size: Int) raises:
-        let new_offset = self.offset + size
-        if new_offset > self.size:
-            raise Error("Resulting offset will be past the end of the FileBuf")
-        if new_offset < 0:
-            raise Error("Resulting offset will be before the beginning of the FileBuf")
-        self.offset = new_offset
+    fn move_offset(inout self, size: Int):
+        self.offset += size
 
-    fn bitcast_offset_float32(inout self, size: Int) raises -> BufferPtrFloat32:
+    fn bitcast_offset_float32(inout self, size: Int) -> BufferPtrFloat32:
         let ret = self.data.offset(self.offset).bitcast[DType.float32]()
-        self.move_offset(size * sizeof[DType.float32]())
+        self.offset += size * sizeof[DType.float32]()
         return ret
 
-    fn get_offset(self) raises -> Int: 
-        if self.offset > self.size:
-            raise Error("Offset is past the end of the FileBuf")
-        if self.offset < 0:
-            raise Error("Offset is before the beginning of the FileBuf")
-        return self.offset
-
 
 struct Tokenizer:
     var vocab: PointerStrings
     var vocab_scores: BufferPtrFloat32
     var max_token_length: Int
     var vocab_size: Int
-    var sorted_vocab: PointerStrings
-    var sorted_indices: DynamicVector[Int]
 
-    fn __init__(inout self, vocab_size: Int, inout buf: FileBuf) raises -> None:
+    fn __init__(inout self, vocab_size: Int):
         self.vocab_size = vocab_size
-        self.max_token_length = read_val_int(buf)
-        self.vocab_scores = BufferPtrFloat32.alloc(self.vocab_size)
-        self.vocab = PointerStrings.alloc(self.vocab_size)
-        # lazy load sorted vocab
-        self.sorted_vocab = PointerStrings.alloc(0)
-        self.sorted_indices = DynamicVector[Int](0)
-
-        # read vocab_scores & vocab values (tokens)
-        for i in range(0, self.vocab_size):
-            self.vocab_scores.store(i, read_val_float32(buf))
-            let slen = read_val_int(buf)
-            self.vocab.store(i, read_val_str(buf, slen))
-
-        return None
-
-    # sort vocab by string_compare
-    fn sort(inout self) -> None:
-        if len(self.sorted_indices) < self.vocab_size:
-            self.sorted_indices = DynamicVector[Int](self.vocab_size)
-            self.sorted_vocab = PointerStrings.alloc(self.vocab_size)
-            for ii in range(self.vocab_size):
-                self.sorted_vocab.store(ii, self.vocab[ii])
-                self.sorted_indices.push_back(ii)
-
-        let n = self.vocab_size
-        quicksort(self.sorted_vocab, self.sorted_indices, 0, n - 1)
-        return None
-
-    # Binary search that returns -1 if string is not found
-    fn find(inout self, token: PointerString) -> Int:
-        let n = self.vocab_size
-        if len(self.sorted_indices) < n:
-            self.sort()
-        var left = 0
-        var right = n - 1
-        while left <= right:
-            let mid = left + (right - left) // 2
-            let comparison = string_compare(self.sorted_vocab[mid], token)
-            if comparison == 0:
-                return self.sorted_indices[mid]
-            if comparison < 0:
-                left = mid + 1
-            else:
-                right = mid - 1
-        return -1
+        self.vocab = PointerStrings.alloc(vocab_size)
+        self.vocab_scores = BufferPtrFloat32.alloc(vocab_size)
+        self.max_token_length = 0
 
 
 struct Config:
     var dim: Int
-    var kv_dim: Int
     var hidden_dim: Int
     var n_layers: Int
     var n_heads: Int
     var n_kv_heads: Int
-    var kv_mul: Int
     var vocab_size: Int
     var seq_len: Int
-    var head_size: Int
 
     fn __init__(inout self):
         self.dim = 0
@@ -349,9 +240,6 @@ struct Config:
         self.n_kv_heads = 0
         self.vocab_size = 0
         self.seq_len = 0
-        self.kv_dim = 0
-        self.kv_mul = 0
-        self.head_size = 0
 
 
 struct RunState:
@@ -361,13 +249,12 @@ struct RunState:
     var hb: Matrix  # buffer for hidden dimension in the ffn (hidden_dim,)
     var hb2: Matrix  # buffer for hidden dimension in the ffn (hidden_dim,)
     var q: Matrix  # query (dim,)
-    var k: Matrix  # key (kv_dim,)
-    var v: Matrix  # value (kv_dim,)
+    var k: Matrix  # key (dim,)
+    var v: Matrix  # value (dim,)
     var att: Matrix  # buffer for scores/attention values (n_heads, seq_len)
     var logits: Matrix  # output logits
-    var key_cache: Matrix  # (layer, seq_len, dim)
-    var value_cache: Matrix  # (layer, seq_len, dim)
-    var rt: Runtime
+    var key_cache: Matrix3  # (layer, seq_len, dim)
+    var value_cache: Matrix3  # (layer, seq_len, dim)
 
     fn __init__(inout self, config: Config):
         self.x = Matrix(config.dim)
@@ -382,17 +269,18 @@ struct RunState:
         self.hb2.alloc_zero()
         self.q = Matrix(config.dim)
         self.q.alloc_zero()
-        self.k = Matrix(0, 0)
-        self.v = Matrix(0, 0)
+        self.k = Matrix(config.dim)
+        self.k.alloc_zero()
+        self.v = Matrix(config.dim)
+        self.v.alloc_zero()
         self.att = Matrix(config.n_heads, config.seq_len)
         self.att.alloc_zero()
         self.logits = Matrix(config.vocab_size)
         self.logits.alloc_zero()
-        self.key_cache = Matrix(config.n_layers, config.seq_len, config.kv_dim)
+        self.key_cache = Matrix3(config.n_layers, config.seq_len, config.dim)
         self.key_cache.alloc_zero()
-        self.value_cache = Matrix(config.n_layers, config.seq_len, config.kv_dim)
+        self.value_cache = Matrix3(config.n_layers, config.seq_len, config.dim)
         self.value_cache.alloc_zero()
-        self.rt = Runtime(num_cores() // 2)
 
 
 struct TransformerWeights:
@@ -400,18 +288,18 @@ struct TransformerWeights:
     var freq_cis_real: Matrix
     var freq_cis_imag: Matrix
     var rms_att_weight: Matrix
-    var wq: Matrix
-    var wk: Matrix
-    var wv: Matrix
-    var wo: Matrix
+    var wq: Matrix3
+    var wk: Matrix3
+    var wv: Matrix3
+    var wo: Matrix3
     var rms_ffn_weight: Matrix
-    var w1: Matrix
-    var w3: Matrix
-    var w2: Matrix
+    var w1: Matrix3
+    var w3: Matrix3
+    var w2: Matrix3
     var rms_final_weight: Matrix
     var wcls: Matrix
 
-    fn __init__(inout self, config: Config, shared_weights: Int, inout buf: FileBuf) raises:
+    fn __init__(inout self, config: Config, shared_weights: Int, inout buf: FileBuf):
         self.token_embedding_table = Matrix(config.vocab_size, config.dim)
         # set buf ptr to buf data from file
         self.token_embedding_table.set_buf_ptr(
@@ -421,23 +309,23 @@ struct TransformerWeights:
         self.rms_att_weight.set_buf_ptr(
             buf.bitcast_offset_float32(self.rms_att_weight.size())
         )
-        self.wq = Matrix(config.n_layers, config.dim, config.dim)
+        self.wq = Matrix3(config.n_layers, config.dim, config.dim)
         self.wq.set_buf_ptr(buf.bitcast_offset_float32(self.wq.size()))
-        self.wk = Matrix(config.n_layers, config.dim, config.kv_dim)
+        self.wk = Matrix3(config.n_layers, config.dim, config.dim)
         self.wk.set_buf_ptr(buf.bitcast_offset_float32(self.wk.size()))
-        self.wv = Matrix(config.n_layers, config.dim, config.kv_dim)
+        self.wv = Matrix3(config.n_layers, config.dim, config.dim)
         self.wv.set_buf_ptr(buf.bitcast_offset_float32(self.wv.size()))
-        self.wo = Matrix(config.n_layers, config.dim, config.dim)
+        self.wo = Matrix3(config.n_layers, config.dim, config.dim)
         self.wo.set_buf_ptr(buf.bitcast_offset_float32(self.wo.size()))
         self.rms_ffn_weight = Matrix(config.n_layers, config.dim)
         self.rms_ffn_weight.set_buf_ptr(
             buf.bitcast_offset_float32(self.rms_ffn_weight.size())
         )
-        self.w1 = Matrix(config.n_layers, config.dim, config.hidden_dim)
+        self.w1 = Matrix3(config.n_layers, config.dim, config.hidden_dim)
         self.w1.set_buf_ptr(buf.bitcast_offset_float32(self.w1.size()))
-        self.w2 = Matrix(config.n_layers, config.dim, config.hidden_dim)
+        self.w2 = Matrix3(config.n_layers, config.dim, config.hidden_dim)
         self.w2.set_buf_ptr(buf.bitcast_offset_float32(self.w2.size()))
-        self.w3 = Matrix(config.n_layers, config.dim, config.hidden_dim)
+        self.w3 = Matrix3(config.n_layers, config.dim, config.hidden_dim)
         self.w3.set_buf_ptr(buf.bitcast_offset_float32(self.w3.size()))
         self.rms_final_weight = Matrix(config.dim)
         self.rms_final_weight.set_buf_ptr(
@@ -487,87 +375,82 @@ fn config_init(inout config: Config, inout buf: FileBuf) raises:
     config.n_kv_heads = read_val_int(buf)
     config.vocab_size = read_val_int(buf)
     config.seq_len = read_val_int(buf)
-    config.head_size = config.dim // config.n_heads
-    config.kv_dim = (config.n_kv_heads * config.dim) // config.n_heads
-    config.kv_mul = config.n_heads // config.n_kv_heads
     return None
 
 
-fn accum(inout a: BufferPtrFloat32, b: BufferPtrFloat32, size: Int) -> None:
-    @parameter
-    fn _acc[_nelts: Int](j: Int):
-        a.offset(j).simd_store[_nelts](
-            0, a.offset(j).simd_load[_nelts](0) + b.offset(j).simd_load[_nelts](0)
-        )
+fn tokenizer_init(inout tok: Tokenizer, inout buf: FileBuf) -> None:
+    tok.max_token_length = read_val_int(buf)
+    tok.vocab_scores = BufferPtrFloat32.alloc(tok.vocab_size)
+    tok.vocab = PointerStrings.alloc(tok.vocab_size)
+
+    # read vocab_scores & vocab values (tokens)
+    for i in range(0, tok.vocab_size):
+        tok.vocab_scores.simd_store[1](i, read_val_float32(buf))
+        let slen = read_val_int(buf)
+        tok.vocab.store(i, read_val_str(buf, slen))
+
+    tok.vocab_scores = buf.data.offset(buf.offset).bitcast[DType.float32]()
+    buf.offset += tok.vocab_size * 4
+    return None
+
 
-    vectorize[nelts, _acc](size)
+fn accum(inout a: BufferPtrFloat32, b: BufferPtrFloat32, size: Int) -> None:
+    for i in range(size):
+        let val = a.offset(i).simd_load[1](0) + b.offset(i).simd_load[1](0)
+        a.offset(i).simd_store[1](0, val)
 
 
 fn rmsnorm(
     inout o: BufferPtrFloat32, x: BufferPtrFloat32, weight: BufferPtrFloat32, size: Int
 ) -> None:
     # Calculate sum of squares
-    var tmp = SIMD[DType.float32, nelts](0)
-
-    @parameter
-    fn _sum2[_nelts: Int](j: Int):
-        if _nelts < nelts:
-            tmp[0] += (x.offset(j).simd_load[_nelts](0) ** 2).reduce_add()
-        else:
-            tmp += x.offset(j).simd_load[nelts](0) ** 2
-
-    vectorize[nelts, _sum2](size)
-
-    var ss: Float32 = tmp.reduce_add()
+    var ss: Float32 = 0.0
+    for i in range(size):
+        let xx = x.offset(i).simd_load[1](0) ** 2
+        ss += xx
     ss = ss / size + 1e-5
     ss = 1.0 / math.sqrt(ss)
-
     # Normalize and scale
-    @parameter
-    fn _norm[_nelts: Int](j: Int):
-        let val = weight.simd_load[_nelts](j) * ss * x.simd_load[_nelts](j)
-        o.offset(j).simd_store[_nelts](0, val)
-
-    vectorize[nelts, _norm](size)
+    for j in range(size):
+        let val = weight.offset(j).simd_load[1](0) * (ss * x.offset(j).simd_load[1](0))
+        o.offset(j).simd_store[1](0, val)
 
 
 fn softmax(inout x: BufferPtrFloat32, size: Int) -> None:
     # Find max value (for numerical stability)
-    var max_val: Float32 = -1e9
-
-    @parameter
-    fn _max[_nelts: Int](j: Int):
-        let val = x.simd_load[_nelts](j).reduce_max()
-        if val > max_val:
-            max_val = val
-
-    vectorize[nelts, _max](size)
-
+    var max_val: Float32 = x.offset(0).simd_load[1](0)
+    for i in range(size):
+        let xi = x.offset(i).simd_load[1](0)
+        if xi > max_val:
+            max_val = xi
     # Exp and sum
     var ssum: Float32 = 0.0
-
-    @parameter
-    fn _sum_exp[_nelts: Int](j: Int):
-        x.simd_store[_nelts](j, math.exp(x.simd_load[_nelts](j) - max_val))
-        ssum += x.simd_load[_nelts](j).reduce_add()
-
-    vectorize[nelts, _sum_exp](size)
-
-    @parameter
-    fn _norm[_nelts: Int](j: Int):
-        x.simd_store[_nelts](j, x.simd_load[_nelts](j) / ssum)
-
-    vectorize[nelts, _norm](size)
-
-
-fn matmul_parallelized(C: Matrix, A: Matrix, B: Matrix, rt: Runtime):
-    @parameter
-    fn compute_row(i: Int):
+    for i in range(size):
+        let xi = x.offset(i).simd_load[1](0)
+        x.offset(i).simd_store[1](0, math.exp(xi - max_val))
+        ssum += x.offset(i).simd_load[1](0)
+    # Normalize
+    for i in range(size):
+        let xi = x.offset(i).simd_load[1](0)
+        x.offset(i).simd_store[1](0, xi / ssum)
+
+
+fn matmul_naive(C: Matrix, x: Matrix, w: Matrix) -> None:
+    # W(d,n) @ X(n,) -> C (d,)
+    # By far the most amount of time is spent inside this little function
+    for i in range(w.rows):
+        C[i] = 0.0
+        for j in range(w.cols):
+            C[i] += x[j] * w[i, j]
+
+
+fn matmul_vectorized(C: Matrix, A: Matrix, B: Matrix):
+    for i in range(0, B.rows):
         var tmp = SIMD[DType.float32, nelts](0)
 
         @parameter
         fn dot[_nelts: Int](j: Int):
-            if _nelts < nelts:  # take care of tail array elements with length <  nelts
+            if _nelts < nelts: # take care of tail array elements with length <  nelts
                 tmp[0] += (A.load[_nelts](j) * B.load[_nelts](i, j)).reduce_add()
             else:
                 tmp += A.load[nelts](j) * B.load[nelts](i, j)
@@ -575,12 +458,28 @@ fn matmul_parallelized(C: Matrix, A: Matrix, B: Matrix, rt: Runtime):
         vectorize[nelts, dot](B.cols)
         C[i] = tmp.reduce_add()
 
-    parallelize[compute_row](rt, B.rows, rt.parallelism_level())
+fn matmul_parallelized(C: Matrix, A: Matrix, B: Matrix):
+    @parameter
+    fn calc_row(i: Int):
+        var T = BufferPtrFloat32.alloc(nelts)
+        var Tbuf = Buffer[nelts, DType.float32](T)
+        memset_zero(T, nelts)
+        @parameter
+        fn dot[nelts: Int](j: Int):
+            T.simd_store[nelts](
+                0, T.simd_load[nelts](0) + A.load[nelts](j) * B.load[nelts](i, j)
+            )
+
+        vectorize[nelts, dot](B.cols)
+        C[i] = sum[nelts, DType.float32](Tbuf)
+
+    parallelize[calc_row](B.rows)
 
 
-fn matmul(inout C: Matrix, A: Matrix, B: Matrix, rt: Runtime) -> None:
+fn matmul(inout C: Matrix, A: Matrix, B: Matrix) -> None:
     # B (d,n) @ A (n,) -> C (d,)
-    matmul_parallelized(C, A, B, rt)
+    matmul_vectorized(C, A, B)
+    # matmul_parallelized(C, A, B)
 
 
 fn transformer(
@@ -594,9 +493,7 @@ fn transformer(
     var x = state.x.data
     let dim = config.dim
     let hidden_dim = config.hidden_dim
-    let head_size = config.head_size
-    let kv_dim = config.kv_dim
-    let kv_mul = config.kv_mul
+    let head_size = dim // config.n_heads
 
     # tmp matrix for matmul operations
     var tmpw = Matrix(0, 0)
@@ -616,43 +513,39 @@ fn transformer(
 
         # QKV matmuls for this position
         tmpw.set_buf_ptr(weights.wq.data.offset(l * dim * dim), dim, dim)
-        matmul(state.q, state.xb, tmpw, state.rt)
+        matmul(state.q, state.xb, tmpw)
 
-        let loff = l * config.seq_len * kv_dim
-        state.k.set_buf_ptr(state.key_cache.data.offset(loff + pos * kv_dim), 1, kv_dim)
-        tmpw.set_buf_ptr(weights.wk.data.offset(l * dim * kv_dim), kv_dim, dim)
-        matmul(state.k, state.xb, tmpw, state.rt)
+        tmpw.set_buf_ptr(weights.wk.data.offset(l * dim * dim), dim, dim)
+        matmul(state.k, state.xb, tmpw)
 
-        state.v.set_buf_ptr(
-            state.value_cache.data.offset(loff + pos * kv_dim), 1, kv_dim
-        )
-        tmpw.set_buf_ptr(weights.wv.data.offset(l * dim * kv_dim), kv_dim, dim)
-        matmul(state.v, state.xb, tmpw, state.rt)
+        tmpw.set_buf_ptr(weights.wv.data.offset(l * dim * dim), dim, dim)
+        matmul(state.v, state.xb, tmpw)
 
         # Apply RoPE rotation to the q and k vectors for each head
-        let q = state.q.data
-        let k = state.k.data
-        for i in range(0, head_size * config.n_kv_heads, 2):
-            let head_dim_half = i % head_size // 2
-            let fcr = freq_cis_real_row.offset(head_dim_half).load(0)
-            let fci = freq_cis_imag_row.offset(head_dim_half).load(0)
-            let q0 = q.offset(i).load(0)
-            let q1 = q.offset(i + 1).load(0)
-            let k0 = k.offset(i).load(0)
-            let k1 = k.offset(i + 1).load(0)
-            q.offset(i).store(0, q0 * fcr - q1 * fci)
-            q.offset(i + 1).store(0, q0 * fci + q1 * fcr)
-            k.offset(i).store(0, k0 * fcr - k1 * fci)
-            k.offset(i + 1).store(0, k0 * fci + k1 * fcr)
-
-        for i in range(head_size * config.n_kv_heads, dim, 2):
-            let head_dim_half = i % head_size // 2
-            let fcr = freq_cis_real_row.offset(head_dim_half).load(0)
-            let fci = freq_cis_imag_row.offset(head_dim_half).load(0)
-            let q0 = q.offset(i).load(0)
-            let q1 = q.offset(i + 1).load(0)
-            q.offset(i).store(0, q0 * fcr - q1 * fci)
-            q.offset(i + 1).store(0, q0 * fci + q1 * fcr)
+        for h in range(config.n_heads):
+            # Get the q and k vectors for this head
+            let q = state.q.data.offset(h * head_size)
+            let k = state.k.data.offset(h * head_size)
+
+            # Rotate q and k by the freq_cis_real and freq_cis_imag
+            for i in range(0, head_size, 2):
+                let q0 = q.offset(i).simd_load[1](0)
+                let q1 = q.offset(i + 1).simd_load[1](0)
+                let k0 = k.offset(i).simd_load[1](0)
+                let k1 = k.offset(i + 1).simd_load[1](0)
+                let fcr = freq_cis_real_row.offset(i // 2).simd_load[1](0)
+                let fci = freq_cis_imag_row.offset(i // 2).simd_load[1](0)
+                q.offset(i).simd_store[1](0, q0 * fcr - q1 * fci)
+                q.offset(i + 1).simd_store[1](0, q0 * fci + q1 * fcr)
+                k.offset(i).simd_store[1](0, k0 * fcr - k1 * fci)
+                k.offset(i + 1).simd_store[1](0, k0 * fci + k1 * fcr)
+
+        # Save key,value at this time step (pos) to our kv cache
+        let loff = l * config.seq_len * dim  # kv cache layer offset for convenience
+        let key_cache_row = state.key_cache.data.offset(loff + pos * dim)
+        let value_cache_row = state.value_cache.data.offset(loff + pos * dim)
+        memcpy[DType.float32](key_cache_row, state.k.data, config.dim)
+        memcpy[DType.float32](value_cache_row, state.v.data, config.dim)
 
         # Multihead attention. Iterate over all heads
         for h in range(config.n_heads):
@@ -665,17 +558,15 @@ fn transformer(
             # Iterate over all timesteps, including the current one
             for t in range(pos + 1):
                 # Get the key vector for this head and at this timestep
-                let k = state.key_cache.data.offset(
-                    loff + t * kv_dim + (h // kv_mul) * head_size
-                )
+                let k = state.key_cache.data.offset(loff + t * dim + h * head_size)
                 # Calculate the attention score as the dot product of q and k
                 var score: Float32 = 0.0
                 for i in range(head_size):
-                    score += q.offset(i).load(0) * k.offset(i).load(0)
+                    score += q.offset(i).simd_load[1](0) * k.offset(i).simd_load[1](0)
                 score /= math.sqrt[DType.float32, 1](head_size)
 
                 # Save the score to the attention buffer
-                att.offset(t).store(0, score)
+                att.offset(t).simd_store[1](0, score)
 
             # Softmax the scores to get attention weights, from 0..pos inclusively
             softmax(att, pos + 1)
@@ -685,18 +576,18 @@ fn transformer(
             memset_zero(xb, head_size)
             for t in range(pos + 1):
                 # Get the value vector for this head and at this timestep
-                let v = state.value_cache.data.offset(
-                    loff + t * kv_dim + (h // kv_mul) * head_size
-                )
+                let v = state.value_cache.data.offset(loff + t * dim + h * head_size)
                 # Get the attention weight for this timestep
-                let a = att.offset(t).load(0)
+                let a = att.offset(t).simd_load[1](0)
                 # Accumulate the weighted value into xb
                 for i in range(head_size):
-                    let xbi = xb.offset(i).load(0) + a * v.offset(i).load(0)
-                    xb.offset(i).store(0, xbi)
+                    let xbi = xb.offset(i).simd_load[1](0) + a * v.offset(i).simd_load[
+                        1
+                    ](0)
+                    xb.offset(i).simd_store[1](0, xbi)
         # Final matrix multiplication to get the output of the attention
         tmpw.set_buf_ptr(weights.wo.data.offset(l * dim * dim), dim, dim)
-        matmul(state.xb2, state.xb, tmpw, state.rt)
+        matmul(state.xb2, state.xb, tmpw)
 
         # Residual connection back into x
         accum(x, state.xb2.data, dim)
@@ -706,10 +597,10 @@ fn transformer(
 
         # Calculate self.w1(x) and self.w3(x) for FFN
         tmpw.set_buf_ptr(weights.w1.data.offset(l * dim * hidden_dim), hidden_dim, dim)
-        matmul(state.hb, state.xb, tmpw, state.rt)
+        matmul(state.hb, state.xb, tmpw)
 
         tmpw.set_buf_ptr(weights.w3.data.offset(l * dim * hidden_dim), hidden_dim, dim)
-        matmul(state.hb2, state.xb, tmpw, state.rt)
+        matmul(state.hb2, state.xb, tmpw)
 
         # Apply SiLU activation function (silu(x) = x * sigmoid(x))
         for i in range(hidden_dim):
@@ -722,7 +613,7 @@ fn transformer(
 
         # Final matrix multiplication to get the output of the FFN
         tmpw.set_buf_ptr(weights.w2.data.offset(l * dim * hidden_dim), dim, hidden_dim)
-        matmul(state.xb, state.hb, tmpw, state.rt)
+        matmul(state.xb, state.hb, tmpw)
 
         # Residual connection
         accum(x, state.xb.data, dim)
@@ -732,7 +623,7 @@ fn transformer(
 
     # Classifier into logits
     tmpw.set_buf_ptr(weights.wcls.data, config.vocab_size, dim)
-    matmul(state.logits, state.x, tmpw, state.rt)
+    matmul(state.logits, state.x, tmpw)
 
 
 fn argmax(v: Matrix) -> Int:
@@ -755,64 +646,12 @@ fn sample(probabilities: Matrix) -> Int:
     var cdf: Float32 = 0.0
     for i in range(n):
         cdf += probabilities[i]
-        if r.load(0) < cdf:
+        if r.simd_load[1](0) < cdf:
             return i
     return n - 1  # In case of rounding errors
 
 
-fn bpe_encode(inout tokens: DynamicVector[Int], text: String, inout tok: Tokenizer):
-    for pos in range(len(text)):
-        let char = str_to_ptr(text[pos])
-        let id = tok.find(char)
-
-        if id == -1:
-            print("Not a good prompt token at pos ", pos)
-            return
-        tokens.push_back(id)
-
-    while True:
-        var best_score = Float32(-1e10)
-        var best_id = -1
-        var best_idx = -1
-
-        for i in range(len(tokens) - 1):
-            # Check if we can merge the pair (tokens[i], tokens[i+1])
-            let str = str_concat(tok.vocab[tokens[i]], tok.vocab[tokens[i + 1]])
-            let id = tok.find(str)
-            if id != -1 and tok.vocab_scores.load(id) > best_score:
-                best_score = tok.vocab_scores.load(id)
-                best_id = id
-                best_idx = i
-
-        if best_idx == -1:
-            # We couldn't find any more pairs to merge, so we're done
-            break
-
-        # Merge the consecutive pair (best_idx, best_idx+1) into new token best_id
-        tokens[best_idx] = best_id
-        # Delete token at position best_idx+1, shift the entire sequence back 1
-        var _tokens = DynamicVector[Int]()
-        for i in range(0, best_idx + 1):
-            _tokens.push_back(tokens[i])
-        for i in range(best_idx + 2, len(tokens)):
-            _tokens.push_back(tokens[i])
-        tokens = _tokens
-
-
-fn str2num(d: Int) -> Int:
-    # covert Hex to decimal
-    if d >= ord("A"):
-        return d - ord("A") + 10
-    return d - ord("0")
-
-
 fn print_str(s: PointerString):
-    # print raw byte like <0x0A>
-    if (s[1].to_int() == ord("0")) and (s[2].to_int() == ord("x")):
-        let d1: Int = s[3].to_int()
-        let d2: Int = s[4].to_int()
-        print_no_newline(chr(str2num(d1) * 16 + str2num(d2)))
-        return
     # print all chars till null character
     var p: Int = 0
     while s[p].to_int() != 0:
@@ -825,73 +664,22 @@ fn time_in_ms() -> Int:
     return time.now() // 1_000_000
 
 
-fn print_usage():
-    print("Usage: mojo llama2.mojo <checkpoint> [options]")
-    print(
-        'Example: mojo llama2.mojo stories15M.bin -s 99 -n 256 -t 0.5 -i "Llama is an'
-        ' animal"'
-    )
-    print("Options:")
-    print("  -s <int>    random seed, default time.now()")
-    print("  -t <float>  temperature in [0,1.0], default 1.0")
-    print("  -n <int>    number of steps to run for, default 256. 0 = max_seq_len")
-    print("  -i <string> input prompt")
-
-
 fn main() raises:
-    print("num hardware threads: ", num_cores())
-    print("SIMD vector width: ", nelts)
-    var tokenizer = StringRef("tokenizer.bin")
-    var checkpoint = StringRef("stories15M.bin")
-    var temperature = 0.9
+    print("num hardware threads: ", num_cores(), " SIMD vector width: ", nelts)
+    let checkpoint = "stories15M.bin"
+    # let checkpoint = "stories110M.bin"
+    let tokenizer = "tokenizer.bin"
+    let temperature = 0.0
     var steps = 256
-    var prompt = String("")
-    var rng_seed: Int = time.now()
-
-    @parameter
-    fn argparse() raises -> Int:
-        let args = argv()
-        if len(args) < 2:
-            return 0
-        checkpoint = args[1]
-        for i in range(2, len(args), 2):
-            if args[i] == "-p":
-                print("Option not supported: ", args[i])
-            if args[i] == "-n":
-                steps = atol(args[i + 1])
-            if args[i] == "-tk":
-                tokenizer = args[i + 1]
-            if args[i] == "-s":
-                rng_seed = atol(args[i + 1])
-            if args[i] == "-i":
-                prompt = args[i + 1]
-            if args[i] == "-t":
-                let val = args[i + 1]
-                temperature = 0.0
-                # hacky parse float, keep only 1 digit
-                for c in range(0, len(val)):
-                    if val[c] == ".":
-                        temperature += atol(val[c + 1]) * 0.1
-                        break
-                    else:
-                        temperature = atol(val[c])
-                if temperature < -1e9 or temperature > (1 + 1e9):
-                    print("Wrong temperature value", temperature)
-                    return 0
-        return 1
-
-    let res = argparse()
-    if res == 0:
-        print_usage()
-        return
-
+    let prompt = ""
+    let rng_seed: Int = time.now()
     random.seed(rng_seed)
     var fbuf: FileBuf = FileBuf()
     var tbuf: FileBuf = FileBuf()
     var config: Config = Config()
 
     read_file(checkpoint, fbuf)
-    print("checkpoint size: ", fbuf.size, "[", fbuf.size // 1024 // 1024, "MB ]")
+    print("checkpoint size: ", fbuf.size)
     config_init(config, fbuf)
 
     # negative vocab size is hacky way of signaling unshared weights. bit yikes.
@@ -902,58 +690,51 @@ fn main() raises:
 
     let weights: TransformerWeights = TransformerWeights(config, shared_weights, fbuf)
 
+    var tok: Tokenizer = Tokenizer(config.vocab_size)
+
     if steps <= 0 or steps > config.seq_len:
         steps = config.seq_len
 
     # Read in the tokenizer.bin file
     read_file(tokenizer, tbuf)
-    var tok = Tokenizer(config.vocab_size, tbuf)
+    tokenizer_init(tok, tbuf)
 
     # Create and initialize the application RunState
     var state = RunState(config)
 
-    # Process the prompt, if any
-    var prompt_tokens = DynamicVector[Int]()
-
-    if prompt:
-        bpe_encode(prompt_tokens, prompt, tok)
-
     # Start the main loop
     var start = 0  # Used to time our code, only initialized after the first iteration
     var next_token = 0  # Will store the next token in the sequence
     # Initialize with token 1 (=BOS), as done in Llama-2 sentencepiece tokenizer
     var token = 1
+    var pos = 0  # Position in the sequence
+    # Explicitly print the initial BOS token for stylistic symmetry reasons
+
+    print("<s>")
 
-    # Position in the sequence
-    var pos = 0
     while pos < steps:
         # Forward the transformer to get logits for the next token
         transformer(token, pos, config, state, weights)
 
-        if pos < len(prompt_tokens):
-            next_token = prompt_tokens[pos]
+        # Sample the next token
+        if temperature == 0.0:
+            # Greedy argmax sampling: take the token with the highest probability
+            next_token = argmax(state.logits)
         else:
-            # Sample the next token
-            if temperature == 0.0:
-                # Greedy argmax sampling: take the token with the highest probability
-                next_token = argmax(state.logits)
-            else:
-                # Apply the temperature to the logits
-                for q in range(config.vocab_size):
-                    state.logits[q] = state.logits[q] / temperature
-                # Apply softmax to the logits to get the probabilities for the next token
-                softmax(state.logits.data, config.vocab_size)
-                # Sample from this distribution to get the next token
-                next_token = sample(state.logits)
-
-            # Finish generating when EOS, BOS appear
-            if next_token == 1 or next_token == 2:
-                break
+            # Apply the temperature to the logits
+            for q in range(config.vocab_size):
+                state.logits[q] = state.logits[q] / temperature
+            # Apply softmax to the logits to get the probabilities for the next token
+            softmax(state.logits.data, config.vocab_size)
+            # Sample from this distribution to get the next token
+            next_token = sample(state.logits)
+
         var token_str: PointerString = tok.vocab[next_token]
         if token == 1 and token_str[0] == ord(" "):
             token_str = token_str.offset(1)
 
         print_str(token_str)
+        # flush?
 
         # Advance forward
         token = next_token
@@ -963,4 +744,4 @@ fn main() raises:
             start = time_in_ms()
 
     let end = time_in_ms()
-    print("\nachieved tok/s: ", (pos - 1) / (end - start) * 1000)
+    print("\nachieved tok/s: ", (steps - 1) / (end - start) * 1000)