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b132de677d
* tinygrad.nn * flake8 * working on pylint * more pylint * more pylint * pylint passes * networkx * mypy can't infer that type * junk
266 lines
12 KiB
Python
266 lines
12 KiB
Python
# type: ignore
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from __future__ import annotations
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import os
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from tinygrad.llops.ops_gpu import GPUBuffer, CL, CLProgram, CLBuffer
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from tinygrad.ops import ProcessingOps
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from tinygrad.helpers import prod, ConvArgs
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from typing import List, Tuple, Optional, Dict, Set
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import numpy as np
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import pyopencl as cl
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UNSAFE_FLOAT4 = int(os.getenv("UNSAFE_FLOAT4", 0))
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import pathlib
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def load(x):
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with open(x) as f:
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ret = f.read()
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return ret
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CONV_SRC = load(pathlib.Path(__file__).parent.parent.parent / 'accel/opencl/conv.cl')
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MATMUL_SRC = load(pathlib.Path(__file__).parent.parent.parent / 'accel/opencl/matmul.cl')
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class ECL(CL):
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@staticmethod
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def image(shape):
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if int(os.getenv("FLOAT16", 0)):
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# HALF_FLOAT breaks tests
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fmt = cl.ImageFormat(cl.channel_order.RGBA, cl.channel_type.HALF_FLOAT)
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else:
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fmt = cl.ImageFormat(cl.channel_order.RGBA, cl.channel_type.FLOAT)
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return cl.Image(CL().cl_ctx, cl.mem_flags.READ_WRITE, fmt, shape=shape)
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def get_replacements(prg_src:str, opencl_type:List[str]) -> Dict[str, str]:
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middle_code = []
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"""
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vv = "xyzw"
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for i in range(4):
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acc = f"outputValues[i].{vv[i%4]}"
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args = [x.split(" ")[-1].replace("*", "") for x in opencl_type]
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args = [f"(outputRow * get_image_width(output) + outputLocation.x)*4+{i}", acc]+args
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middle_code.append(f"{acc} = _ewop("+', '.join(args)+");\n")
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"""
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acc = "outputValues[i]"
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args = [x.split(" ")[-1].replace("*", "") for x in opencl_type]
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args = ["smp", "outputLocation", "(outputLocation.y * get_image_width(output) + outputLocation.x)*4", acc]+args
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middle_code.append(f"{acc} = _ewop("+', '.join(args)+");\n")
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replacements = {}
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replacements["//PREFIX"] = prg_src
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replacements["//BINOP"] = ''.join(middle_code)
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if len(opencl_type) != 0:
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replacements["//ARGS"] = ","+','.join(opencl_type)
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return replacements
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def roundup(x, n=4): return (x+(n-1))//n * n
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class OpenCLBuffer(GPUBuffer):
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def __init__(self, shape, hostbuf:Optional[OpenCLBuffer]=None, backing:Optional[np.ndarray]=None):
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self._image = hostbuf._image if hostbuf is not None else None
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super().__init__(shape, hostbuf, backing)
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@staticmethod
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def fromCPU(x): return OpenCLBuffer(x.shape, backing=x.view(np.ndarray).astype(np.float32).ravel())
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def __repr__(self): return f"<OpenCLBuffer with shape {self.shape!r}>"
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@property
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def cl(self):
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if self._buf is None:
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if self._backing is not None:
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self._buf = CLBuffer(4*roundup(prod(self._backing.shape)))
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CL.enqueue_copy(self._buf.cl, self._backing, is_blocking=False)
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elif self.st.contiguous:
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self._buf = CLBuffer(4*roundup(prod(self.shape)))
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if self._image is not None:
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self._buf = CLBuffer(4*roundup(prod(self._image.shape)*4))
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if self._backing is not None:
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CL.enqueue_copy(self._buf.cl, self._backing, is_blocking=False)
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#self._backing = None
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#print(f"converting {self.shape} back to buffer, image shape is {self._image.shape}")
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CLProgram("from_image", """
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__kernel void from_image(
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read_only image2d_t in,
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__global float4 *out) {
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const sampler_t smp = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
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int2 l;
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l.y = get_global_id(1);
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l.x = get_global_id(0);
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int W = get_image_width(in);
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out[l.y*W + l.x] = read_imagef(in, smp, l);
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}
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""")(self._image.shape, None, self._image, self._buf.cl)
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self._image = None
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return self._buf.cl
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def is_image(self): return self._image is not None
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@property
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def image(self):
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if self._image is None:
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assert self.shape[2] == 4 and len(self.shape) == 3
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self._image = ECL.image(shape=(self.shape[1], self.shape[0]))
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if self._buf is not None:
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assert prod(self.shape) == prod(self._image.shape)*4
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#print(f"converting {self.shape} to image with shape {self._image.shape}")
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CLProgram("to_image", """
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__kernel void to_image(
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__global const float4 *in,
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write_only image2d_t out) {
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int2 l;
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l.y = get_global_id(1);
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l.x = get_global_id(0);
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int W = get_image_width(out);
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write_imagef(out, l, in[l.y*W + l.x]);
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}
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""")(self._image.shape, None, self._buf.cl, self._image)
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self._buf = None
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return self._image
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SUPPORTS_PADDING = True
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def processing_op(x, op:ProcessingOps, w:GPUBuffer, C:ConvArgs):
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assert op == ProcessingOps.CONV, f"{op} isn't supported"
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return type(x)(C.out_shape)._processing_op([("input", x.contiguous_op()), ("weight", w.contiguous_op())], "acc", C)
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seen = set()
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def _processing_op(ret, bufs: List[Tuple[str, OpenCLBuffer]]=[], code:str="acc", C=None, start="0.0", reduce_shape=None, earlybufs:Set[str]=set(), earlycode:str="acc"):
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if C is None or earlycode != "acc":
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# TODO: handle an opencl conv without the conv part
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return super()._processing_op(bufs, code, C, start, reduce_shape, earlybufs, earlycode)
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assert earlycode == "acc"
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assert start == "0.0"
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x = [x for x in bufs if x[0] == "input"][0][1]
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w = [x for x in bufs if x[0] == "weight"][0][1]
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ewbufs = [x for x in bufs if x[0] not in ["input", "weight"]]
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if (x,w) in OpenCLBuffer.seen:
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print("WARNING: recomputing CONV with", x, w)
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OpenCLBuffer.seen.add((x,w))
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fakebufs = []
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ewtypes = []
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getters = []
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for name, buf in ewbufs:
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view, unfolded = buf.contiguous_view_constant_fold(name)
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if not unfolded:
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getters.append(view)
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fakebufs.append(name)
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getters.append(f"inline float4 get4_{name}(int gid) {{"+
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f"return (float4)(get_{name}(gid+0), get_{name}(gid+1), get_{name}(gid+2), get_{name}(gid+3)); }}")
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elif buf.is_image() and buf.shape == ret.shape and buf.st.contiguous:
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# use an image here
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ewtypes.append(f"read_only image2d_t {name}_g")
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getters.append(f"inline float4 get4_{name}(read_only image2d_t x, const sampler_t smp, int2 loc, int gid) {{ return read_imagef(x, smp, loc); }}")
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elif buf.st.contiguous:
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# use float4
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ewtypes.append(f"__global const float4 *{name}_g")
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getters.append(f"inline float4 get4_{name}(__global const float4 *x, const sampler_t smp, int2 loc, int gid) {{ return x[gid/4]; }}")
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elif UNSAFE_FLOAT4:
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# aggressive constant folding
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fakebufs.append(name)
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prt = buf._backing.reshape((-1, 4))
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cc = []
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for ii in range(prt.shape[0]): cc.append("(float4)(%ff, %ff, %ff, %ff)" % (prt[ii][0], prt[ii][1], prt[ii][2], prt[ii][3]))
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getters.append(f"const __constant float4 const_{name}[] = {{"+', '.join(cc)+"};")
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getters.append(f"inline float4 get4_{name}(int gid) {{"+
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"int idx = gid;"+buf.st.expr()+";"+
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f"return const_{name}[idx/4]; }}")
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"""
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# use float4 indexed (HACK!)
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# TODO: work out when this is okay
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ewtypes.append(f"__global const float4 *{name}_g")
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getters.append(f"inline float4 get4_{name}(__global const float4 *x, const sampler_t smp, int2 loc, int gid) {{"+
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"int valid = 1; int idx = gid;"+buf.st.expr()+";"+
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f"return x[idx/4]; }}")
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"""
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else:
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# fallback to float
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getters.append(view)
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ewtypes.append(f"__global const float *{name}_g")
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getters.append(f"inline float4 get4_{name}(__global const float *x, const sampler_t smp, int2 loc, int gid) {{"+
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f"return (float4)(get_{name}(x,gid+0), get_{name}(x,gid+1), get_{name}(x,gid+2), get_{name}(x,gid+3)); }}")
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# remove fakebufs
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ewbufs = [x for x in ewbufs if x[0] not in fakebufs]
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elementwise_prefix = '\n'.join(getters)+ \
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"\n\ninline float4 _ewop("+','.join(["const sampler_t smp", "int2 loc", "int gid", "float4 acc"]+ewtypes)+") {\n"+ \
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''.join([f"float4 {name} = get4_{name}(gid);\n" for name in fakebufs])+ \
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''.join([f"float4 {name} = get4_{name}({name}_g, smp, loc, gid);\n" for name, _ in ewbufs])+ \
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f"return {code}; }}"
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replacements = get_replacements(elementwise_prefix, ewtypes)
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x, w = x.contiguous_op(), w.contiguous_op()
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options = []
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if C.bs > 1:
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options.append("-DBATCH")
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assert C.py == 0, "batched conv doesn't work with y-padding"
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if C.sx == 1 and C.sy == 1 and C.dx == 1 and C.dy == 1 and C.cin == 1: options.append("-DDEPTHWISE_UNSTRIDED")
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elif C.cin == 1: options.append("-DDEPTHWISE")
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if int(os.getenv("MATMUL", 0)) and C.groups == 1 and C.H == 1 and C.W == 1 and C.iy == 1 and C.ix == 1 and C.oy == 1 and C.ox == 1 and C.sx == 1 and C.sy == 1 and C.dx == 1 and C.dy == 1:
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options.append("-DMATMUL")
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# NOTE: this is not actually a matmul, it's a vector * matrix
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conv_args = []
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conv_short_names = ["numPackedInputChannelsForGroup", "totalNumPackedInputChannels", "numPackedOutputChannelsForGroup", "totalNumPackedOutputChannels", "numOutputColumns", "numOutputRows", "numInputRows"]
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conv_shorts = [max(1, C.cin//4), C.groups*C.cin//4, max(1, C.rcout//4), C.cout//4, C.ox, C.oy, C.iy]
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conv_src = MATMUL_SRC
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replacements["//SHORTS"] = ''.join([f"short {name} = {val};" for name,val in zip(conv_short_names, conv_shorts)])
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if "//BINOP" in replacements:
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replacements["//BINOP"] = replacements["//BINOP"].replace("outputValues[i]", "outputValues")
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for k,v in replacements.items():
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conv_src = conv_src.replace(k, v)
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#print(conv_src)
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conv_prg = CLProgram("matmul", conv_src,
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options=tuple(options),
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argdtypes=tuple([None, None, None, None] + [np.int16]*len(conv_args) + [None]*len(ewbufs))
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)
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global_work_size = [4, 16, C.cout//4]
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# must be even
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lw = CL.cl_ctx.devices[0].max_work_group_size // (global_work_size[0] * global_work_size[1])
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while global_work_size[2] % lw != 0:
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lw -= 1
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local_work_size = [4, global_work_size[1], lw]
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#print(global_work_size, local_work_size)
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conv_prg(global_work_size, local_work_size, cl.LocalMemory(4 * local_work_size[0] * local_work_size[1] * lw), x.image, w.image, ret.image, *conv_args, *[buf.image if 'image2d_t' in typ else buf.cl for typ, (_, buf) in zip(ewtypes, ewbufs)])
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return ret
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# this option is unused
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if C.H == 1 and C.W == 1: options.append("-DONLY_1X1_CONV")
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assert C.cout%4 == 0
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conv_src = CONV_SRC
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conv_short_names = ["filterSizeX", "filterSizeY", "paddingX", "paddingY", "strideX", "strideY", "dilationX", "dilationY"]
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conv_shorts = [C.W, C.H, C.px, C.py, C.sx, C.sy, C.dx, C.dy]
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conv_arg_names = ["numPackedInputChannelsForGroup", "totalNumPackedInputChannels", "numPackedOutputChannelsForGroup", "totalNumPackedOutputChannels", "numOutputColumns", "numOutputRows", "numInputRows"]
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conv_args = [max(1, C.cin//4), C.groups*C.cin//4, max(1, C.rcout//4), C.cout//4, C.ox, C.oy, C.iy]
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NUM_OUTPUTS = 4
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options.append(f"-DNUM_OUTPUTS={NUM_OUTPUTS}")
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# comment out for args
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conv_short_names += conv_arg_names
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conv_shorts += conv_args
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conv_args = []
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options.append("-DNOARGS")
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replacements["//SHORTS"] = ''.join([f"short {name} = {val};" for name,val in zip(conv_short_names, conv_shorts)])
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for k,v in replacements.items():
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conv_src = conv_src.replace(k, v)
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#print(conv_src)
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conv_prg = CLProgram("image_conv", conv_src,
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options=tuple(options),
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argdtypes=tuple([None, None, None] + [np.int16]*len(conv_args) + [None]*len(ewbufs))
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)
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global_work_size = [C.cout//4, (C.ox+NUM_OUTPUTS-1)//NUM_OUTPUTS, C.bs*C.oy]
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conv_prg(global_work_size, None, x.image, w.image, ret.image, *conv_args, *[buf.image if 'image2d_t' in typ else buf.cl for typ, (_, buf) in zip(ewtypes, ewbufs)])
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return ret
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GPUBuffer = OpenCLBuffer
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