* start cpu threading
* fix
* fix2
* fix
* hacks?
* threads
* minor
* no dsp
* dsp 2
* n
* more
* test
* xm
* cleaner
* readable
* f
* reorder
* when no threads
* rangeify
* typos
* not needed
* reapply
* remoev this
* linter
* fixed cpu count in ci
* fix
* fixes
* rm
* typo
* sort based on speed
* test if test works in ci
* Revert "test if test works in ci"
This reverts commit 1f05edb531.
* do not pad thread
* load llama3-1B to WEBGPU device
* include compile script for loading llama3 to WEBGPU
* parametrize max_context in build_transformer fxn
* jit_model with two different args sets
* compile for webgpu, split weights
* load model weight parts in browser
* export all tensors from initialized transformer
* run transformer inference in browser
* enable tiktoken with llama bpe in browser
* count total tokens on client with tiktoken.js
* full client-side chat streaming, eliminate server
* revert change that enabled jitting with 2 argsets
* llama without Variable or cache_kv, for webgpu
* have client use mask tokens / whole context
* cleanup staged weights
* add tiktoken.js build script, README
* export CLANG for Q6_k to float32 decompression
* fix and test exported CLANG code for Q6_k to fp32
* revert changes to jit and export_model
* isolate clang export
* test Q6_K to float32 decompression in browser
* gguf_load now also returns t_infos and data_start
* prepare llama-1B Q6_K gguf chunks for browser
* cache and decompress quantized llama in browser
* enable separate deployment of large files
* fix kv cache and symbolic with llama wgpu
* eliminate browser lag during decompression
* hash metadata and weight chunks
* delete obsolete indexeddb cache to free disk
* add progress bar, track model download/decompress
* refactor progress callback
* skip buffer hash verification for speed
* Display progress for entire loading scope
* Report page load errors to user
* actually display errors
* skip prompt tokens already seen by model
* skip prefilling with last assistant message tokens
* on page load tell user if webgpu not enabled
* push deployed URL root to window.history
* make note of bug sources with TODO items
* isolate bug in CLANG with BEAM=2
* remove clang_bug.py from diff
* decompress q6k to f32 on webgpu instead of clang
* remove unused code
* inter-weight decomp with larger wgpu kernels
* parallelize decompression submissions
* refactor dequantize scheduling
* add progress bar back
* fix bug
* temp fix for loading GGUF Q6_K to fp16 not fp32
* fix rendering of exported CLANG
* remove weight casts, sketch js functions for clang
* get symbolic vars from jit_cache for model export
* include symbolic vars in exported CLANG
* render js for clang transformer
* toggle clang/webgpu deployment; refactor decomp
* compile and render clang Q6_K->fp16 and int8 quant
* fix rendered clang for abs(fp16), to work in wasm
* simplify clang js wrapping
* run compiled clang in worker
* prepare llama weights in workers, q6k to int8/fp16
* tinychat on clang in browser, f32/int8 weights
* move wasm inference to (now flexible) worker
* don't load redundant embeddings
* modest wasm perf gain with compile flags
* set default backend, enable backend choice/backup
* render symbolic vars in exported WEBGPU
* quantize webgpu llama to int8/f32
* improve UX arising from rendered WEBGPU
* clean up webgpu launch
* new weights split: smaller chunks, tinygrad quant.
* switch webgpu inference to int8 quant
* remove unneeded clang decompression
* eliminate unneeded kv cache transfer to wasm
* use 1 worker for simplified clang decompression
* display launch errors
* refactor: stream load weight chunks to WebGPU
* show loading chunk completion
* quantize embeddings to int8
* test float16 as input for quantization
* webgpu: use f16 source, int8 embed, eliminate q6k
* simplify split weights prep: all from state_dict
* revert change to nn.state.gguf_load
* remove unneeded decompression from webgpu client
* remove unneeded code
* decrease dl chunks from 47 to 16 MiB
* improve stability of webgpu loading on mobile
* autodetect mobile, improve load stability
* refactor: progress closure
* refactor: one unified progress bar
* remove unneeded code
* revert changes to tinygrad core library
* enforce ios18.3 nerfed max buf size
* BEAM=3 webgpu
* cache integrity, mobile save throttling
* improve mobile UX - no autozoom on prompt box
* clang: int8 from f16, remove q6k
* reduce concurrent dls on mobile to 2 for stability
* refactor: wasm backend with stream loading
* prevent race between wasm load and indexedb save
* split wasm kernels into separate modules
* js wrapper for multiple wasm module inference
* revert multi-module wasm to single module
* make mobile wasm load more stable/fast
* refactor: copy weights into wasm without crashes
* fix bug in download queue; increase mobile dls
* refactor exported clang wrapper, split weights
* remove unnecessary code
* greatly improve int8 quant quality with rounding
* eliminate mobile throttling
* increase webgpu context to 4096 tokens
* export webgpu js functions
* enable separate hosted weights for mobile/pc
* enable prompt-thread switching during generation
* stop generation when max_context is reached
* show progress bar for prefill
* tell user if webgpu fails, while wasm loads
* make loading messages more concise
* update font
* revert changes to tinychat python app launch
* cleanup quantization, add scale_dtype param
* cleanup kv cache code
* cleanup compile code
* link tok_embeddings with output in webgpu export
* refactor: export_model webgpu: symbolic vars
* refactor: export_model weight loading
* forgot to commit export_model.py
* change CLANG to CPU
* deal with pylint incorrectly failing tests
* simplify f-strings for older CI python version
* fix pre-python3.12 parser errors
* [Int32Array] not Int32Array
* cleanup webgpu compile after refactor export_model
* refactor WASM export into export_model
* merge WebGPU/WASM compile scripts
* simplify max_contexts for local deployment
* fix parser issues and whitespace
* deduplicate variable defs for non-wasm clang export
* cleanup code
* cleanup compile scripts
* simplify wasm inference wrapping
* simplify webgpu symbolic vars export
* refactor: unify export of symbolic variables
* simplify WASM export
* simplify clang/wasm export
* update README and build scripts
* separate files for browser/python apps
* restore original python tinychat app files
* browser and python tinychats share assets
* minor cleanup
* isolate compile/export model
---------
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
* Start from andredaprato:webgpu-clean
* Fix infs
* inf wgsl function is not needed
* Emulated ulong for threefry, more tests passing
* Randomness tests passing
* Update model export to support new changes in webgpu, efficientnet export works again
* Simplify shift emulation in wgsl
* Delete test file
* Fix bigger than u32 u32 literal
* Why was skip copies added here?
* Python3.12 for webgpu tests
* Fix model export syntax error
* Get test ops passing with some skips
* Fix lint
* Much simpler shift
* Run more tests
* Timestamp queries are not supported in CI, so skip search tests
* All fancy indexing passing
* r is ctx
* Run more dtype tests by using is_dtype_supported
* Cleanup ulong shift rendering
* UPat -> Pat, UOps -> Ops
* Pat -> UPat
* Refactor render_ushift if-else
* Pattern to avoid ulong mul
* Remove vals_dtype
* is_nan trick + rewrite, test_isnan passing
* Rewrite a * select(1, nan, gate) -> select(a, nan, gate)
* No arg, just op
* Support char, uchar, short, ushort
* Run test_index_mnis now that we have uint8
* Fix pyling
* Save 3 lines by using base Compiler
* No more long emulation
* Remove fixup_binops
* No more external_local_bufx wgsl specific cstyle modif, use base extra_pm
* Simpler, faster copyin/out
* Skip some new tests that use long
* Fix typo
* copyout touchup
* Save lines by using render_cast
* WebGL is not supported in core, delete it from is_dtype_supported
* More narrow test skips for some unary tests
* TernaryOps, UnaryOps -> Ops
* TinyGrad supports WebGPU
* StableDiffusion demo: f16tof32 gpu is a lib, update UI
* Packed load/store, no more scale_size, no core tinygrad changes
* Rename copyin, copyout
* Device -> dev
* Fix lint
* Pattern matcher rule for packed load/store
* Refactor
* Shorter packed load/store
* this should fix lint
* Fix mypy
* SD compile script working
* New SD webgpu UI
* New default prompt
* New SD weights
* Fix title when webgpu not available
* Run symbolic tests, simplify is_nan, use round_up
* Show step time on UI
* Bump minimum wgpu version to v0.19
* Fix latent
---------
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
* refactor to Program class
* switch to Program
* fix tests
* smaller diff
* self.p
* more tests
* fix metal test
* tests
* fix openpilot
* move that to linearizer
* p.launchdims
* WebGL WIP
* 84% of ops passing test
* tests passing 100%
* Cleanup, refactor
* Shave off some lines
* Work on dtypes
* TestOps at 100% again
* Efficient net shaders compile in browser webgl2
* Compile all efficientnet shaders in browser
* Create empty textures for tensor buffers
* Run program. Up next weight loading
* Exported WebGL model working
* Add tests, refactor
* Explicit cast alu for GLSL
* Fix CI tests
* WebGL efficientnet demo
* Compile and run yolov8 in browser
* Fix imports
* Simplify yolo compile
* Fix bool*bool and cast cmplt to float
* More tests
* Do std tests pass on CI?
* Skip std tests on CI
* Remove explicit_cast_alu hack, and solve it in code_for_op
* Move to new dtype-less alloc api
* Remove local size hack: optimize local_size only if device has local
* Remove glsl.py, and move content to cstyle
* dont_use_locals in opts
* Fix dtype tests
* type_map in CStyleLanguage
* Make core changes smaller, cleaner, refactor export_model and demo
* Skip pad_slice
* Simplify: render_const, render_conditional
* solve bool alu for other binops, cleaner ops_webgl
* Fix noopt hack
* Remove some skipIfs
* WebGL image hack
* type_names is a better name
* global_max
* Fix dtype import
* Fix type_names -> type_map
* Fix lint
* Remove webgpu, back to 5k lines (#3040)
* remove webgpu
* max 5000 lines
* revert those to master
* retain that cstyle
---------
Co-authored-by: Ahmed Harmouche <ahmedharmouche92@gmail.com>
* lazy rewrite, try 2
* min fix tests
* pass contig test
* put broken pads back
* move that to realize
* no contig child fixes array packing
* so wrong
* now that's correct
* base children
* fix bind issues
* disable to_image_idx
* fix tests
* that failure shouldn't break other tests
* more fixes
* fix torch
* skip failing tests in CI
* 1e-7
* half is broken
* 1e-6 margin of error
* cpu tests pass
* torch works
* works
* metal works
* fix ops_disk
* metal jit works
* fix openpilot
* llvm and clang work
* fix webgpu
* docs are rly broken
* LRU works on metal
* delete comment
* revert name to ._buf. LRU only on Compiled
* changes
* allocator
* allocator, getting closer
* lru alloc
* LRUAllocator
* all pass
* metal
* cuda
* test examples
* linearizer
* test fixes
* fix custom + clean realize
* fix hip
* skip tests
* fix tests
* fix size=0
* fix MOCKHIP
* fix thneed
* copy better
* simple
* old style metal copy
* fix thneed
* np reshape
* give cuda a device
* metal indirect command buffers
* sub 1ms gpt
* metal batch exec is good
* remove whitespace
* input_replace
* fix ci
* useResources
* very simple cacheallocator
* update_stats
* fix CI
* minor
* remove that from jit
* var_vals are global
* working with global ish
* better
* fix export model
* fix tests
* better kv cache
* does it run?
* use where for kvmask
* fix excessive var_vals
* fix import
* how does multigpu use this?
* llama kinda work
* faster and simpler
* cleanup
* fix conversation mode
* test cleanups
* fix one more test
* test cleanup
---------
Co-authored-by: George Hotz <geohot@gmail.com>
* Enable Multi-Output Export
* Add test
* Update examples and lint
* fix padding
* test ops
* dummy commit to rerun test
* revert cuda lint
* Enforce tuple/list of tensors
* subscripted generics
* put back webgpu test
* Re-enable WebGPU Efficientnet test
* Allow multi-input model export
* Add model export unit test
* Fix efficientnet compilation
* Only run model export test on JIT supported devices
* Skip export model test if not EXPORT_SUPPORTED_DEVICE
* Symbolic Shape JIT
update tests
2 variables symbolic ops, adding more tests
test passing
cleanup
* more test cases
* single flag
* review update
* jit attention one piece
* realize
* symbolic_jit test for cuda
* old artifact
* works with cuda gpu but failed ci
* CUDACPU
