tarry.singh/Radix
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

docs: first model – fix const/var bug and enforce 80‑column width

Browse Source
This commit is contained in:
Foke Singh 2023-01-06 10:34:44 +00:00
parent eded305649
commit fab1c93d5b
1 changed files with 32 additions and 17 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

49
docs/tutorials/write_first_model.md
View File

@ -137,7 +137,7 @@ You see, in ZML AI models are just structs with a forward function!
There are more things to observe:
- forward functions typically take Tensors as inputs, and return Tensors.
- more advanced use-cases are passing in / returning structs or tuples, like
- more advanced use-cases are passing in / returning structs or tuples, like
`struct { Tensor, Tensor }` as an example for a tuple of two tensors.
You can see such use-cases, for example in the
[Llama Model](https://github.com/zml/zml/tree/master/examples/llama)
@ -251,12 +251,15 @@ const model_shapes = try zml.aio.populateModel(Layer, allocator, bs);
// Start compiling. This uses the inferred shapes from the BufferStore.
// The shape of the input tensor, we have to pass in manually.
var compilation = try async_(zml.compileModel, .{ allocator, model_shapes, .forward, .{input_shape}, platform });
var compilation = try async_(
zml.compileModel,
.{ allocator, model_shapes, .forward, .{input_shape}, platform },
);
// Produce a bufferized weights struct from the fake BufferStore.
// This is like the inferred shapes, but with actual values.
// We will need to send those to the computation device later.
const model_weights = try zml.aio.loadBuffers(Layer, .{}, bs, arena, platform);
var model_weights = try zml.aio.loadBuffers(Layer, .{}, bs, arena, platform);
defer zml.aio.unloadBuffers(&model_weights); // for good practice
// Wait for compilation to finish
@ -305,11 +308,15 @@ in device memory.
```zig
// prepare an input buffer
// Here, we use zml.HostBuffer.fromSlice to show how you would create a HostBuffer
// with a specific shape from an array.
// For situations where e.g. you have an [4]f16 array but need a .{2, 2} input shape.
// Here, we use zml.HostBuffer.fromSlice to show how you would create a
// HostBuffer with a specific shape from an array.
// For situations where e.g. you have an [4]f16 array but need a .{2, 2} input
// shape.
var input = [3]f16{ 5.0, 5.0, 5.0 };
var input_buffer = try zml.Buffer.from(platform, zml.HostBuffer.fromSlice(input_shape, &input));
var input_buffer = try zml.Buffer.from(
platform,
zml.HostBuffer.fromSlice(input_shape, &input),
);
defer input_buffer.deinit();
// call our executable module
@ -469,33 +476,41 @@ pub fn asyncMain() !void {
.buffers = buffers,
};
// A clone of our model, consisting of shapes. We only need shapes for compiling.
// We use the BufferStore to infer the shapes.
// A clone of our model, consisting of shapes. We only need shapes for
// compiling. We use the BufferStore to infer the shapes.
const model_shapes = try zml.aio.populateModel(Layer, allocator, bs);
// Start compiling. This uses the inferred shapes from the BufferStore.
// The shape of the input tensor, we have to pass in manually.
var compilation = try async_(zml.compileModel, .{ allocator, model_shapes, .forward, .{input_shape}, platform });
var compilation = try async_(
zml.compileModel,
.{ allocator, model_shapes, .forward, .{input_shape}, platform },
);
// Produce a bufferized weights struct from the fake BufferStore.
// This is like the inferred shapes, but with actual values.
// We will need to send those to the computation device later.
const model_weights = try zml.aio.loadBuffers(Layer, .{}, bs, arena, platform);
defer zml.aio.unloadBuffers(&model_weights);
var model_weights = try zml.aio.loadBuffers(Layer, .{}, bs, arena, platform);
defer zml.aio.unloadBuffers(&model_weights); // for good practice
// Wait for compilation to finish
const compiled = try compilation.await_();
// pass the model weights to the compiled module to create an executable module
// pass the model weights to the compiled module to create an executable
// module
var executable = try compiled.prepare(arena, model_weights);
defer executable.deinit();
// prepare an input buffer
// Here, we use zml.HostBuffer.fromSlice to show how you would create a HostBuffer
// with a specific shape from an array.
// For situations where e.g. you have an [4]f16 array but need a .{2, 2} input shape.
// Here, we use zml.HostBuffer.fromSlice to show how you would create a
// HostBuffer with a specific shape from an array.
// For situations where e.g. you have an [4]f16 array but need a .{2, 2}
// input shape.
var input = [3]f16{ 5.0, 5.0, 5.0 };
var input_buffer = try zml.Buffer.from(platform, zml.HostBuffer.fromSlice(input_shape, &input));
var input_buffer = try zml.Buffer.from(
platform,
zml.HostBuffer.fromSlice(input_shape, &input),
);
defer input_buffer.deinit();
// call our executable module