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Update tutorial and example code to use the new asyncc name and Generic slugs.

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This commit is contained in:
Foke Singh 2023-05-08 16:58:45 +00:00
parent 5543c8192f
commit 672df8fa2f
6 changed files with 21 additions and 25 deletions
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8
docs/tutorials/write_first_model.md
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@ -102,7 +102,7 @@ const zml = @import("zml");
const asynk = @import("async");
// shortcut to the async_ function in the asynk module
const async_ = asynk.async_;
const asyncc = asynk.asyncc;
```
You will use above lines probably in all ZML projects. Also, note that **ZML is
@ -251,7 +251,7 @@ 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_(
var compilation = try asyncc(
zml.compileModel,
.{ allocator, model_shapes, .forward, .{input_shape}, platform },
);
@ -420,7 +420,7 @@ const std = @import("std");
const zml = @import("zml");
const asynk = @import("async");
const async_ = asynk.async_;
const asyncc = asynk.asyncc;
/// Model definition
const Layer = struct {
@ -482,7 +482,7 @@ pub fn asyncMain() !void {
// 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_(
var compilation = try asyncc(
zml.compileModel,
.{ allocator, model_shapes, .forward, .{input_shape}, platform },
);

4
examples/benchmark/main.zig
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@ -3,7 +3,7 @@ const zml = @import("zml");
const asynk = @import("async");
const flags = @import("tigerbeetle/flags");
const async_ = asynk.async_;
const asyncc = asynk.asyncc;
// set log level to debug to print the generated IR
pub const std_options = .{
@ -92,7 +92,7 @@ pub fn asyncMain() !void {
// Start compiling.
// The shape of the input tensor, we have to pass in manually.
timer.reset();
var compilation = try async_(zml.module.compileModel, .{ allocator, Benchmark{}, .forward, .{ a_shape, b_shape }, platform });
var compilation = try asyncc(zml.module.compileModel, .{ allocator, Benchmark{}, .forward, .{ a_shape, b_shape }, platform });
// Wait for compilation to finish
const compiled = try compilation.await_();

6
examples/llama/main.zig
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@ -6,8 +6,6 @@ const asynk = @import("async");
const flags = @import("tigerbeetle/flags");
const llama_mod = @import("llama.zig");
const async_ = asynk.async_;
const LlamaLM = llama_mod.LlamaLM;
const Llama = llama_mod.Llama;
const KvCache = llama_mod.KvCache;
@ -241,8 +239,8 @@ pub fn asyncMain() !void {
const rng_shape = Tensor.Rng.shape();
const compile_start = std.time.milliTimestamp();
var fut_mod_prefill = try async_(zml.compile, .{ allocator, LlamaLM, .{llama_options}, .forward, .{ tokens_shape, token_idx_shape, null, rng_shape }, ts, platform });
var fut_mod = try async_(zml.compile, .{ allocator, LlamaLM, .{llama_options}, .forward, .{ tokens_shape, token_idx_shape, kv_cache_shape, rng_shape }, ts, platform });
var fut_mod_prefill = try asynk.asyncGeneric(zml.compile, .{ allocator, LlamaLM, .{llama_options}, .forward, .{ tokens_shape, token_idx_shape, null, rng_shape }, ts, platform });
var fut_mod = try asynk.asyncGeneric(zml.compile, .{ allocator, LlamaLM, .{llama_options}, .forward, .{ tokens_shape, token_idx_shape, kv_cache_shape, rng_shape }, ts, platform });
log.info("Starting loading weights", .{});
var llama_weights = try zml.aio.loadBuffers(LlamaLM, .{llama_options}, ts, model_arena, platform);

2
examples/loader/main.zig
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@ -2,7 +2,7 @@ const std = @import("std");
const zml = @import("zml");
const asynk = @import("async");
const async_ = asynk.async_;
const asyncc = asynk.asyncc;
pub fn main() !void {
var gpa = std.heap.GeneralPurposeAllocator(.{}){};

4
examples/mnist/mnist.zig
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@ -2,8 +2,6 @@ const std = @import("std");
const zml = @import("zml");
const asynk = @import("async");
const async_ = asynk.async_;
const show_mlir = true;
/// Model definition
@ -115,7 +113,7 @@ pub fn asyncMain() !void {
} else {
std.debug.print("Compiling model to MLIR....\r", .{});
}
var compilation = try async_(zml.compile, .{ allocator, Mnist, .{}, .forward, .{zml.Shape.init(.{ 28, 28 }, .u8)}, buffer_store, platform });
var compilation = try asynk.asyncGeneric(zml.compile, .{ allocator, Mnist, .{}, .forward, .{zml.Shape.init(.{ 28, 28 }, .u8)}, buffer_store, platform });
// Wait for end of compilation and end of weights loading.
const compiled_mnist = try compilation.await_();

4
examples/simple_layer/main.zig
View File

@ -2,7 +2,7 @@ const std = @import("std");
const zml = @import("zml");
const asynk = @import("async");
const async_ = asynk.async_;
const asyncc = asynk.asyncc;
/// Model definition
const Layer = struct {
@ -65,7 +65,7 @@ pub fn asyncMain() !void {
// 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 asyncc(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.