GPU llops

docs/design

@@ -1,18 +1,18 @@
 Getting the core instruction set correct is the value of tinygrad
 
-
+Max size tensor is 6-D for the pool2d
 
 Unary Ops
 ===
 
 These are the simplest to reason about, and have pointwise mem access.
+A and B are always the same size
 
 Forward          :    A      -> B
 Backward (binary):   (B', A) -> A'
 
 
 
-
 Reduce Ops (with axis)
 ===
 

tinygrad/llops/gpu.py

@@ -0,0 +1,141 @@
+# llops don't know about derivatives
+
+import functools
+import numpy as np
+import pyopencl as cl
+from tinygrad.helpers import binary_broadcast
+
+i32 = np.int32
+
+cl_ctx, cl_queue = None, None
+def require_init_gpu():
+  global cl_ctx, cl_queue
+  if cl_ctx is None:
+    devices = cl.get_platforms()[0].get_devices(device_type=cl.device_type.GPU)
+    if len(devices) == 0:
+      devices = cl.get_platforms()[0].get_devices(device_type=cl.device_type.CPU)
+    cl_ctx = cl.Context(devices=devices)
+    # this is an in-order command queue
+    cl_queue = cl.CommandQueue(cl_ctx)
+
+class GPUBuffer:
+  def __init__(self, shape, hostbuf=None):
+    require_init_gpu()
+    self.shape, self.dtype = tuple(shape), np.float32
+    self.cl = hostbuf.cl if isinstance(hostbuf, GPUBuffer) else \
+      cl.Buffer(cl_ctx, cl.mem_flags.READ_WRITE | (cl.mem_flags.COPY_HOST_PTR if hostbuf is not None else 0), 4*np.prod(shape),
+                hostbuf=hostbuf.astype(np.float32).ravel() if hostbuf is not None else None)
+
+  def __repr__(self):
+    return f"<GPUBuffer with shape {self.shape!r}>"
+
+  @staticmethod
+  def fromCPU(x):
+    return GPUBuffer(x.shape, x.view(np.ndarray))
+
+  def toCPU(self):
+    data = np.empty(self.shape, dtype=np.float32)
+    cl_queue.finish()
+    cl.enqueue_copy(cl_queue, data, self.cl, is_blocking=True)
+    return data
+
+def buffer_new(ctx, shape, zero=False):
+  return GPUBuffer(shape, hostbuf=None if not zero else np.zeros(shape, dtype=np.float32))
+
+def buffer_np(ctx, x):
+  return cl.Buffer(cl_ctx, cl.mem_flags.READ_WRITE | cl.mem_flags.COPY_HOST_PTR, hostbuf=x)
+
+def clbuffer(hostbuf, shape):
+  return cl.Buffer(cl_ctx, cl.mem_flags.READ_WRITE | (cl.mem_flags.COPY_HOST_PTR if hostbuf is not None else 0),
+            4*np.prod(shape),
+            hostbuf=hostbuf.astype(np.float32).ravel() if hostbuf is not None else None)
+
+@functools.lru_cache
+def clbuild(name, prg):
+  clprg = cl.Program(cl_ctx, prg).build().__getattr__(name)
+  def run(*args):
+    clprg(cl_queue, *args)
+  return run
+
+# x -> ret
+def unary_op(ctx, code, x):
+  ret = buffer_new(ctx, x.shape)
+  unop = clbuild("unop", """
+  __kernel void unop(__global const float *a_g, __global float *res_g) {
+    int gid = get_global_id(0);
+    float a = a_g[gid];
+    res_g[gid] = """+code+""";
+  }""")
+  unop([np.prod(ret.shape)], None, x.cl, ret.cl)
+  return ret
+
+@functools.lru_cache
+def get_binop_prg(cl_ctx, code, complist):
+  ndims = len(complist)
+  args = "".join([f", int d{i}" for i in range(ndims)] + [f", int p{i}" for i in range(ndims-1)])
+  compute_idx_rets = "".join([f"\n    int idx_ret{i} = (gid0 / {f'p{i}' if i < ndims-1 else '1'}) % d{i};" for i in range(ndims)])
+
+  idx_exprs = ["0", "0"] # [idx_x, idx_y]
+  for i in range(ndims):
+    for j in range(2):
+      if complist[i][j]:
+        idx_exprs[j] = "idx_ret%d + d%d*(%s)" % (i, i, idx_exprs[j])
+
+  return cl.Program(cl_ctx, """__kernel void binop(__global const float *x_g, __global const float *y_g, __global float *res_g"""+args+""") {
+    int gid0 = get_global_id(0);"""+compute_idx_rets+"""
+    float a = x_g["""+idx_exprs[0]+"""];
+    float b = y_g["""+idx_exprs[1]+"""];
+    res_g[gid0] = """+code+""";\n}""").build()
+
+def binary_op(ctx, code, x, y):
+  shape_ret, dimlist, complist = binary_broadcast(x.shape, y.shape)
+  prod_list = np.array(dimlist, dtype=i32)[-1::-1].cumprod(dtype=i32)[-1::-1] # take cumprod from back to front
+
+  prg = get_binop_prg(cl_ctx, code, tuple(complist))
+  ret = buffer_new(ctx, shape_ret, zero=True)
+  prg.binop(cl_queue, [prod_list[0]] if len(dimlist) > 0 else [1], None, x.cl, y.cl, ret.cl, *dimlist, *(prod_list[1:]))
+  return ret
+
+def reduce_op(ctx, code, code2, inp, axis=None, start="0.0"):
+  if axis is None:
+    # full reduce
+    osize = [1]*len(inp.shape)
+  else:
+    osize = np.array(inp.shape)
+    osize[list(axis)] = 1
+  ret = buffer_new(ctx, osize)
+  if axis is None:
+    ret.shape = (1,)
+
+  # TODO: this is insanely slow
+  reduce = clbuild("reduce", """
+  __kernel void reduce(__global const float *a_g, int sz, __global float *res_g, int prod, int n_dims,
+                       __global const int *shape_x, __global const int *shape_ret) {
+    int gid = get_global_id(0);
+
+    float out = """+start+""";
+    for (int x = 0; x < sz; x++) {
+      int idx = 0;  // compute index into a_g
+      int tprod = prod;
+      int tsz = sz;
+      for (int dim = 0; dim < n_dims; dim++) {
+        idx *= shape_x[dim];
+        if (shape_x[dim] == shape_ret[dim]) {   // dim from gid, don't reduce
+          tprod /= shape_x[dim];
+          idx += (gid / tprod) % shape_x[dim];
+        } else {  // dim from x
+          tsz /= shape_x[dim];
+          idx += (x / tsz) % shape_x[dim];
+        }
+      }
+      float a = a_g[idx];
+      """+code+""";
+    }
+    res_g[gid] = """+code2+""";
+  }""")
+  reduce([np.prod(osize)], None, inp.cl,
+    i32(np.prod(inp.shape)//np.prod(osize)), ret.cl,
+    i32(np.prod(osize)), i32(len(osize)),
+    buffer_np(ctx, np.array(inp.shape, dtype=np.int32)),
+    buffer_np(ctx, np.array(osize, dtype=np.int32)))
+  return ret

tinygrad/ops/ops_gpu.py

@@ -1,50 +1,7 @@
-import functools
 import pyopencl as cl
 import numpy as np
-from tinygrad.helpers import binary_broadcast
 from ..tensor import Function
-
-cl_ctx, cl_queue = None, None
-def require_init_gpu():
-  global cl_ctx, cl_queue
-  if cl_queue is None:
-    devices = cl.get_platforms()[0].get_devices(device_type=cl.device_type.GPU)
-    if len(devices) == 0:
-      devices = cl.get_platforms()[0].get_devices(device_type=cl.device_type.CPU)
-    cl_ctx = cl.Context(devices=devices)
-    # this is an in-order command queue
-    cl_queue = cl.CommandQueue(cl_ctx)
-
-class GPUBuffer:
-  def __init__(self, shape, hostbuf=None):
-    require_init_gpu()
-    self.shape, self.dtype = tuple(shape), np.float32
-    self.cl = hostbuf.cl if isinstance(hostbuf, GPUBuffer) else \
-      cl.Buffer(cl_ctx, cl.mem_flags.READ_WRITE | (cl.mem_flags.COPY_HOST_PTR if hostbuf is not None else 0), 4*np.prod(shape),
-                hostbuf=hostbuf.astype(np.float32).ravel() if hostbuf is not None else None)
-
-  def __repr__(self):
-    return f"<GPUBuffer with shape {self.shape!r}>"
-
-  @staticmethod
-  def fromCPU(x):
-    return GPUBuffer(x.shape, x.view(np.ndarray))
-
-  def toCPU(self):
-    data = np.empty(self.shape, dtype=np.float32)
-    cl_queue.finish()
-    cl.enqueue_copy(cl_queue, data, self.cl, is_blocking=True)
-    return data
-
-def buffer_new(ctx, shape, zero=False):
-  return GPUBuffer(shape, hostbuf=None if not zero else np.zeros(shape, dtype=np.float32))
-
-def buffer_np(ctx, x):
-  return cl.Buffer(cl_ctx, cl.mem_flags.READ_WRITE | cl.mem_flags.COPY_HOST_PTR, hostbuf=x)
-
-@functools.lru_cache
-def clbuild(cl_ctx, name, prg):
-  return cl.Program(cl_ctx, prg).build().__getattr__(name)
+from ..llops.gpu import GPUBuffer, clbuild, buffer_new, buffer_np, unary_op, binary_op, reduce_op
 
 def uint2(x, y):
   return np.array((x,y), dtype=cl.cltypes.uint2)
@@ -52,17 +9,6 @@ i32 = np.int32
 
 # ************* unary ops *************
 
-def unary_op(ctx, code, x):
-  ret = buffer_new(ctx, x.shape)
-  unop = clbuild(cl_ctx, "unop", """
-  __kernel void unop(__global const float *a_g, __global float *res_g) {
-    int gid = get_global_id(0);
-    float a = a_g[gid];
-    res_g[gid] = """+code+""";
-  }""")
-  unop(cl_queue, [np.prod(ret.shape)], None, x.cl, ret.cl)
-  return ret
-
 class ReLU(Function):
   def forward(ctx, input):
     ctx.save_for_backward(input)
@@ -93,50 +39,6 @@ class Exp(Function):
 
 # ************* reduce ops *************
 
-def reduce_op(ctx, code, code2, inp, axis=None, start="0.0"):
-  if axis is None:
-    # full reduce
-    osize = [1]*len(inp.shape)
-  else:
-    osize = np.array(inp.shape)
-    osize[list(axis)] = 1
-  ret = buffer_new(ctx, osize)
-  if axis is None:
-    ret.shape = (1,)
-
-  # TODO: this is insanely slow
-  reduce = clbuild(cl_ctx, "reduce", """
-  __kernel void reduce(__global const float *a_g, int sz, __global float *res_g, int prod, int n_dims,
-                       __global const int *shape_x, __global const int *shape_ret) {
-    int gid = get_global_id(0);
-
-    float out = """+start+""";
-    for (int x = 0; x < sz; x++) {
-      int idx = 0;  // compute index into a_g
-      int tprod = prod;
-      int tsz = sz;
-      for (int dim = 0; dim < n_dims; dim++) {
-        idx *= shape_x[dim];
-        if (shape_x[dim] == shape_ret[dim]) {   // dim from gid, don't reduce
-          tprod /= shape_x[dim];
-          idx += (gid / tprod) % shape_x[dim];
-        } else {  // dim from x
-          tsz /= shape_x[dim];
-          idx += (x / tsz) % shape_x[dim];
-        }
-      }
-      float a = a_g[idx];
-      """+code+""";
-    }
-    res_g[gid] = """+code2+""";
-  }""")
-  reduce(cl_queue, [np.prod(osize)], None, inp.cl,
-    i32(np.prod(inp.shape)//np.prod(osize)), ret.cl,
-    i32(np.prod(osize)), i32(len(osize)),
-    buffer_np(ctx, np.array(inp.shape, dtype=np.int32)),
-    buffer_np(ctx, np.array(osize, dtype=np.int32)))
-  return ret
-
 class Sum(Function):
   def forward(ctx, input, axis=None):
     ctx.save_for_backward(input.shape)
@@ -162,33 +64,6 @@ class Max(Function):
 
 # ************* binary ops *************
 
-@functools.lru_cache
-def get_binop_prg(cl_ctx, code, complist):
-  ndims = len(complist)
-  args = "".join([f", int d{i}" for i in range(ndims)] + [f", int p{i}" for i in range(ndims-1)])
-  compute_idx_rets = "".join([f"\n    int idx_ret{i} = (gid0 / {f'p{i}' if i < ndims-1 else '1'}) % d{i};" for i in range(ndims)])
-
-  idx_exprs = ["0", "0"] # [idx_x, idx_y]
-  for i in range(ndims):
-    for j in range(2):
-      if complist[i][j]:
-        idx_exprs[j] = "idx_ret%d + d%d*(%s)" % (i, i, idx_exprs[j])
-
-  return cl.Program(cl_ctx, """__kernel void binop(__global const float *x_g, __global const float *y_g, __global float *res_g"""+args+""") {
-    int gid0 = get_global_id(0);"""+compute_idx_rets+"""
-    float a = x_g["""+idx_exprs[0]+"""];
-    float b = y_g["""+idx_exprs[1]+"""];
-    res_g[gid0] = """+code+""";\n}""").build()
-
-def binary_op(ctx, code, x, y):
-  shape_ret, dimlist, complist = binary_broadcast(x.shape, y.shape)
-  prod_list = np.array(dimlist, dtype=i32)[-1::-1].cumprod(dtype=i32)[-1::-1] # take cumprod from back to front
-
-  prg = get_binop_prg(cl_ctx, code, tuple(complist))
-  ret = buffer_new(ctx, shape_ret, zero=True)
-  prg.binop(cl_queue, [prod_list[0]] if len(dimlist) > 0 else [1], None, x.cl, y.cl, ret.cl, *dimlist, *(prod_list[1:]))
-  return ret
-
 def unbroadcast(ctx, out, in_sh):
   sum_axis = [i for i in range(len(in_sh)) if in_sh[i]==1 and out.shape[i]>1] if in_sh != (1,) else None
   return reduce_op(ctx, "out += a", "out", out, sum_axis)
@@ -254,7 +129,7 @@ class Reshape(Function):
 def perm_axis(ctx, inp, order):
   osize = np.array(inp.shape)[list(order)]
   ret = buffer_new(ctx, osize)
-  perm = clbuild(cl_ctx, "perm", """
+  perm = clbuild("perm", """
   __kernel void perm(__global const float *a_g, __global float *res_g, int n_axis,
                        __global const int *shape, __global const int *order) {
     int gid = get_global_id(0);
@@ -268,7 +143,7 @@ def perm_axis(ctx, inp, order):
     }
     res_g[gid] = a_g[idx];
     }""")
-  perm(cl_queue, [np.prod(osize)], None, inp.cl, ret.cl, i32(len(osize)),
+  perm([np.prod(osize)], None, inp.cl, ret.cl, i32(len(osize)),
     buffer_np(ctx, np.array(inp.shape, dtype=np.int32)),
     buffer_np(ctx, np.array(order, dtype=np.int32)))
   return ret
@@ -286,7 +161,7 @@ def inner_slice(ctx, x, arg):
   shift = [y[0] for y in arg]
   oshape = [y[1]-y[0] for y in arg]
   ret = buffer_new(ctx, oshape)
-  gslice = clbuild(cl_ctx, "gslice", """
+  gslice = clbuild("gslice", """
   __kernel void gslice(__global const float *input, __global float *output, int prod, int n_dims,
                        __global const int *shape_x, __global const int *shape_ret,
                        __global const int *shift) {
@@ -301,7 +176,7 @@ def inner_slice(ctx, x, arg):
     }
     output[gid] = zero ? input[iptr] : 0.0;
   }""")
-  gslice(cl_queue, [np.prod(ret.shape)], None,
+  gslice([np.prod(ret.shape)], None,
     x.cl, ret.cl, i32(np.prod(ret.shape)), i32(len(ret.shape)),
     buffer_np(ctx, np.array(x.shape, dtype=np.int32)),
     buffer_np(ctx, np.array(ret.shape, dtype=np.int32)),
@@ -327,7 +202,7 @@ class Matmul(Function):
     isize, msize, osize = i32(input.shape[-2]), i32(input.shape[-1]), i32(weight.shape[-1])
     ret = buffer_new(ctx, list(input.shape[0:-2])+[isize, osize])
 
-    matmul = clbuild(cl_ctx, "matmul", """
+    matmul = clbuild("matmul", """
     __kernel void matmul(
       __global const float *input, __global const float *weight, __global float *res,
       int isize, int is0, int is1, int msize, int ws0, int ws1, int osize
@@ -348,7 +223,7 @@ class Matmul(Function):
     ctx.save_for_backward(input, weight, matmul, cnt)
 
     # (isize,msize) x (msize,osize) = (isize,osize)
-    matmul(cl_queue, [isize, osize, cnt], None,
+    matmul([isize, osize, cnt], None,
       input.cl, weight.cl, ret.cl, isize,
       msize, i32(1), msize, i32(1), osize, osize)
     return ret
@@ -361,12 +236,12 @@ class Matmul(Function):
     grad_weight = buffer_new(ctx, weight.shape)
 
     # (isize,osize) x (msize,osize) = (isize,msize)
-    matmul(cl_queue, [isize, msize, cnt], None,
+    matmul([isize, msize, cnt], None,
       grad_output.cl, weight.cl, grad_input.cl, isize,
       osize, i32(1), osize, osize, i32(1), msize)
 
     # (isize,msize) x (isize,osize) = (msize,osize)
-    matmul(cl_queue, [msize, osize, cnt], None,
+    matmul([msize, osize, cnt], None,
       input.cl, grad_output.cl, grad_weight.cl, msize,
       i32(1), msize, isize, i32(1), osize, osize)
 
@@ -392,7 +267,7 @@ class Conv2D(Function):
     # weight = (groups, rcout, cin, H, W)
     # output = (bs, groups, rcout, oy, ox)
 
-    conv = clbuild(cl_ctx, "conv", """
+    conv = clbuild("conv", """
     __kernel void conv(__global const float *input, __global const float *weight, __global float *output,
       int H, int W, int groups, int rcout, int cin, int oy, int ox, int iy, int ix, int ys, int xs) {
 
@@ -417,7 +292,7 @@ class Conv2D(Function):
       output[B*groups*rcout*oy*ox + g*rcout*oy*ox + c*oy*ox + Y*ox + X] = acc;
     }""")
 
-    conv(cl_queue, [bs*groups*rcout, oy, ox], None,
+    conv([bs*groups*rcout, oy, ox], None,
       x.cl, w.cl, ret.cl,
       i32(H), i32(W), i32(groups), i32(rcout), i32(cin),
       i32(oy), i32(ox), i32(iy), i32(ix), i32(ys), i32(xs)
@@ -442,7 +317,7 @@ class Conv2D(Function):
     # tensw = (groups*rcout, cin, H, W)
     # ggg = (bs, groups*rout, oy, ox)
 
-    convw = clbuild(cl_ctx, "convw", """
+    convw = clbuild("convw", """
     __kernel void convw(__global const float *tensx, __global const float *ggg, __global float *dw,
       int H, int W, int groups, int rcout, int cin, int oy, int ox, int iy, int ix, int ys, int xs, int bs) {
 
@@ -463,7 +338,7 @@ class Conv2D(Function):
       }
       dw[get_global_id(0)*H*W + y*W + x] = acc;
     }""")
-    convx = clbuild(cl_ctx, "convx", """
+    convx = clbuild("convx", """
     __kernel void convx(__global const float *tensw, __global const float *ggg, __global float *dx,
       int H, int W, int groups, int rcout, int cin, int oy, int ox, int iy, int ix, int ys, int xs, int bs) {
 
@@ -489,6 +364,6 @@ class Conv2D(Function):
     """)
 
     conv_args = i32(H), i32(W), i32(ctx.groups), i32(rcout), i32(cin), i32(oy), i32(ox), i32(iy), i32(ix), i32(ys), i32(xs), i32(bs)
-    convw(cl_queue, [ctx.groups*rcout*cin, H, W], None, x.cl, grad_output.cl, dw.cl, *conv_args)
-    convx(cl_queue, [bs, ctx.groups, cin], None, w.cl, grad_output.cl, dx.cl, *conv_args)
+    convw([ctx.groups*rcout*cin, H, W], None, x.cl, grad_output.cl, dw.cl, *conv_args)
+    convx([bs, ctx.groups, cin], None, w.cl, grad_output.cl, dx.cl, *conv_args)
     return dx, dw