mirror of
https://github.com/tinygrad/tinygrad.git
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114 lines
8 KiB
Python
114 lines
8 KiB
Python
from typing import Callable, List, Tuple, Any, Dict, cast, Union, Optional, Set
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from weakref import ref
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from collections import defaultdict
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import functools, itertools
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from tinygrad.helpers import DEBUG, DType, merge_dicts, ImageDType
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from tinygrad.ops import RawBuffer, Device, BasicBatchExecutor, ASTRunner
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from tinygrad.tensor import Tensor
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from tinygrad.shape.shapetracker import ShapeTracker
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from tinygrad.shape.symbolic import Variable
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JIT_SUPPORTED_DEVICE = ["GPU", "CLANG", "METAL", "CUDA", "HIP", "WEBGPU", "LLVM"]
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class TinyJit:
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def __init__(self, fxn:Callable):
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self.fxn: Callable = fxn
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self.cnt: int = 0
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self.jit_cache: List[Tuple[Any, List[Optional[RawBuffer]], Dict[Variable, int]]] = []
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self.ret: Any = None
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self.input_replace: Dict[Tuple[int, int], Tuple[Union[int, str], ShapeTracker, DType]]= {} # (kernel_number, buffer_number) -> (input_name, expected_shapetracker, expected_type)
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self.batch_executor: Any = None
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self.updatable_entries: Dict[int, List[int]] = defaultdict(list) # (kernel_number) -> list(argument id). These are buffers from input + variables.
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# add support for instance methods
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def __get__(self, obj, objtype): return functools.partial(self.__call__, obj)
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def __call__(self, *args, **kwargs) -> Any:
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if Device.DEFAULT not in JIT_SUPPORTED_DEVICE: return self.fxn(*args, **kwargs) # only jit on supported device
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# NOTE: this cast is needed since although we know realize will create a ".realized" RawBuffer, the type checker doesn't
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input_rawbuffers: Dict[Union[int, str], Tuple[RawBuffer, ShapeTracker]] = {cast(Union[int, str], k):(cast(RawBuffer, v.realize().lazydata.realized), v.lazydata.st) for k,v in itertools.chain(enumerate(args), kwargs.items()) if v.__class__ is Tensor}
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assert len(input_rawbuffers) != 0, "no inputs to JIT"
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assert len(set(input_rawbuffers.values())) == len(input_rawbuffers), "duplicate inputs to JIT"
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if self.cnt >= 2:
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try: var_vals: Dict[Variable, int] = kwargs["jit_ctx"]
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except KeyError: var_vals = merge_dicts([arg.lazydata.var_vals for arg in args if arg.__class__ is Tensor])
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if len(var_vals) > 1: var_vals = dict(sorted(var_vals.items(), key=lambda kv: kv[0].key))
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for (j,i),(input_name, expected_st, expected_type) in self.input_replace.items():
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assert input_rawbuffers[input_name][0].dtype == expected_type, f"type mismatch in JIT, {input_rawbuffers[input_name][0].dtype} != {expected_type}"
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# NOTE: if we pass jit_ctx instead of using reshape to update the var_vals, we cannot compare the shapetracker directly
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if "jit_ctx" not in kwargs: assert input_rawbuffers[input_name][1].views == expected_st.views, f"ShapeTracker.views mismatch in JIT, {input_rawbuffers[input_name][1].views} != {expected_st.views}"
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self.jit_cache[j][1][i] = input_rawbuffers[input_name][0]
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for j in self.updatable_entries.keys():
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for k in self.jit_cache[j][2].keys():
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try: self.jit_cache[j][2][k] = var_vals[k]
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except KeyError: pass
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self.batch_executor.exec(self.jit_cache, self.updatable_entries)
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for (j,i) in self.input_replace.keys(): self.jit_cache[j][1][i] = None
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elif self.cnt == 1:
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CacheCollector.start()
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self.ret = self.fxn(*args, **kwargs)
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self.jit_cache = CacheCollector.finish()
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assert len(self.jit_cache) != 0, "didn't JIT anything!"
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if DEBUG >= 1: print(f"JIT captured {len(self.jit_cache)} kernels with {len(input_rawbuffers)} inputs")
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# get the inputs for replacement
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for j_,cache in enumerate(self.jit_cache): # type: Tuple[int, Tuple[Callable, List[Optional[RawBuffer]], Dict[Variable, int]]]
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for i,a in enumerate(cache[1]):
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if a in [v[0] for v in input_rawbuffers.values()]:
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self.input_replace[(j_,i)] = [(k, v[1], v[0].dtype) for k,v in input_rawbuffers.items() if v[0] == a][0]
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self.updatable_entries[j_].append(i)
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for i in range(len(cache[2])): self.updatable_entries[j_].append(len(cache[1])+i)
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#if prg.local_size is None: prg.local_size = prg.optimize_local_size(args, preserve_output=True) # the JIT can optimize local
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assert set([x[0] for x in self.input_replace.values()]) == set(input_rawbuffers.keys()), "some input tensors not found"
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self.batch_executor = self.jit_cache[0][0].batch_exec(self.jit_cache) if hasattr(self.jit_cache[0][0], 'batch_exec') else BasicBatchExecutor(self.jit_cache)
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for (j,i) in self.input_replace.keys(): self.jit_cache[j][1][i] = None
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elif self.cnt == 0:
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self.ret = self.fxn(*args, **kwargs)
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self.cnt += 1
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return self.ret
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class _CacheCollector:
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class _Placeholder:
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def __init__(self, buf): self.size, self.dtype, self._device, self.ref, self.buftype = buf.size, buf.dtype, getattr(buf, '_device', None), ref(buf), type(buf)
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def alloc_rawbuf(self): return self.buftype(self.size, self.dtype, **({'device':self._device} if self._device is not None else dict()))
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def __init__(self):
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self.cache: Optional[List[Tuple[Callable, List[Any], Dict[Any,Any]]]] = None
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self.placeholders: Dict[ref[RawBuffer], _CacheCollector._Placeholder] = {} # Output rawbufs are replaced with placeholders to allow freeing of the real buffer while collecting cache.
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self.circular_signatures: Set[Any] = set()
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def start(self): self.cache, self.placeholders, self.circular_signatures = [], {}, set()
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def add(self, prg, rawbufs, var_vals):
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if self.cache is None: return
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# Substitute output buffers with placeholders to find the most optimal reusage.
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if ref(rawbufs[0]) not in self.placeholders: self.placeholders[ref(rawbufs[0])] = _CacheCollector._Placeholder(rawbufs[0])
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cached_rawbufs = [self.placeholders.get(ref(buf), buf) if isinstance(prg, ASTRunner) and isinstance(buf, RawBuffer) and ref(buf) not in self.circular_signatures else buf for buf in rawbufs]
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self.cache.append((prg, cached_rawbufs, var_vals))
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def finish(self):
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if self.cache is None: return []
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rawbuf_pool: List[Tuple[RawBuffer, List[Tuple[int, int]]]] = []
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buf_usage_bounds: Dict[_CacheCollector._Placeholder, Tuple[int, int]] = {}
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buf_map: Dict[_CacheCollector._Placeholder, RawBuffer] = {}
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for j,(_,bufs,_) in enumerate(self.cache):
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for buf in bufs:
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if buf.__class__ is not _CacheCollector._Placeholder: continue
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if buf.ref() is not None: buf_map[buf] = buf.ref() # rawbufs that are referenced are not replaced but are used as is.
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else: buf_usage_bounds[buf] = buf_usage_bounds.get(buf, (j, j))[0], j
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# The query list contains a query for every placeholder that should be replaced with the actual rawbuffer. Queries are served from the largest to the smallest.
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# For each query, find any rawbuffer that is free within the query timeframe or allocate a new one.
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query_list = sorted([(buf.size*buf.dtype.itemsize, buf_usage_bounds[buf][0], buf_usage_bounds[buf][1], buf) for buf in buf_usage_bounds.keys()], key=lambda x: x[0], reverse=True)
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for _, start, end, buf in query_list:
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pool_idx = next((i for i,(with_buf, usages) in enumerate(rawbuf_pool) if self._can_substitute(buf, with_buf) and self._no_intersect(start,end,usages)), -1)
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if pool_idx == -1: rawbuf_pool.append((buf.alloc_rawbuf(), []))
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buf_map[buf] = rawbuf_pool[pool_idx][0]
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rawbuf_pool[pool_idx][1].append((start, end))
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cache_result = [(p, [buf_map.get(buf, buf) for buf in cached_bufs], var_vals) for p, cached_bufs, var_vals in self.cache]
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self.cache = None
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return cache_result
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def _no_intersect(self, start:int, end:int, usages:List[Tuple[int, int]]): return all(en < start or end < st for st, en in usages)
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def _can_substitute(self, buf, with_buf): return buf._device==with_buf._device and (buf.size*buf.dtype.itemsize<=with_buf.size*with_buf.dtype.itemsize if not isinstance(buf.dtype, ImageDType) and not isinstance(with_buf.dtype, ImageDType) else buf.size==with_buf.size and buf.dtype==with_buf.dtype and buf.dtype.shape==with_buf.dtype.shape)
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def _mark_output_buffer(self, output_buffer): self.circular_signatures.add(ref(output_buffer))
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CacheCollector = _CacheCollector()
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