some work on conv

lib/runtime/function.cc

@@ -168,6 +168,7 @@ function::caller function::autotune(driver::stream* stream, const grid_fn_ty& gr
     for(auto it: opt_space_.defines)
       cpp.AddMacro(it.first, &opt.defines.at(it.first));
     cpp.Process(tokens);
+
     // parse
     Parser parser(tokens);
     parser.Parse();
@@ -220,7 +221,7 @@ std::unique_ptr<driver::module> function::make_bin(ir::module &module, driver::c
   codegen::transform::cts cts;
   codegen::generator isel(&axes, &layouts, &align, &allocation, target.get(), opt.num_warps);
   // run passes
-//    ir::print(module, std::cout);
+    ir::print(module, std::cout);
   dce.run(module);
 //  ir::print(module, std::cout);
 
@@ -266,7 +267,6 @@ R"(
 #define bool _Bool
 #define true 1
 #define false 0
-#define __bool_true_false_are_defined 1
 
 #define __readonly      __attribute__((readonly))
 #define __writeonly     __attribute__((writeonly))

python/examples/conv.py

@@ -0,0 +1,11 @@
+import torch
+import triton
+
+N, C, K = 32, 32, 32
+H, W = 32, 32
+R, S = 3, 3
+a = torch.randn(N, C, H, W).cuda()
+b = torch.randn(C, R, S, K).cuda()
+#c = torch.nn.functional.conv2d(a, b)
+c = triton.ops.conv(a, b)
+print(c)

python/examples/dot.py

@@ -53,8 +53,8 @@ def run_torch():
   triton_da = a.grad.clone()
   triton_db = b.grad.clone()
 
-  nanosec = triton.bench_registry[triton_d]
-  print('TFLOPS:', 2. * M * N * K / nanosec * 1e-3)
+  #nanosec = triton.bench_registry[triton_d]
+  #print('TFLOPS:', 2. * M * N * K / nanosec * 1e-3)
   print('Diff DA:', (torch_da - triton_da).max())
   print('Diff DB:', (torch_db - triton_db).max())
 

python/src/bindings.cc

@@ -514,6 +514,7 @@ void gen_torch_make_handles(std::ostream &os,
 }
 
 void gen_torch_make_launch_function(std::ostream &os, const std::vector<ir::argument*>& args) {
+  os << "  std::cout << 9 << std::endl;";
   os << "  std::function<void()> run = [&](){\n  ";
   os << "    (*id_fn_map.at(id))({";
   for(unsigned i = 0; i < args.size() ; i++){
@@ -528,6 +529,7 @@ void gen_torch_make_launch_function(std::ostream &os, const std::vector<ir::argu
   os << "}, *id_grid_map.at(id), &stream);\n";
   os << "  };\n  ";
   os << "  run();";
+  os << "  std::cout << 10 << std::endl;";
   os << "  if(bench > 0)\n  ";
   os << "    i64scalar_map[bench_id] = triton::tools::bench(run, &stream);\n  ";
   }
@@ -586,10 +588,14 @@ extern std::map<size_t, int64_t> i64scalar_map;
 
   gen_torch_signature(oss, fn, outputs, name);
   oss << " {" << std::endl;
+  oss << " std::cout << 1 << std::endl;";
   gen_torch_init_driver(oss, fn->args());
   gen_torch_make_handles(oss, fn->args());
+  oss << "  std::cout << 2 << std::endl;";
   gen_torch_make_launch_function(oss, fn->args());
+  oss << "  std::cout << 3 << std::endl;";
   gen_torch_ret(oss, outputs);
+  oss << " std::cout << \"done\" << std::endl;\n";
   oss << "}" << std::endl;
 
   oss << std::endl;

python/triton/function.py

@@ -83,28 +83,25 @@ class function(metaclass = function_meta):
   @classmethod
   def apply_tensorflow(cls, *args, **kwargs):
     ctx = OpContext()
-    result = cls.forward(ctx, *args, **kwargs)
 
-    # check that all the results stem from triton.empty
-    # and get the corresponding TF tensors if possible
+    # run forward pass
+    result = cls.forward(ctx, *args, **kwargs)
     result = result if isinstance(result, tuple) else (result, )
     result = function.extract_tf_tensors(result, 'forward')
     
     # Register backward pass
-    key = result[0]
     op = result[0].op
-    ctx_registry[key] = ctx
-    remap_in = cls.map_in_to_args(op, args)
-    remap_out = cls.map_res_to_out(op, result)
-    name = op.op_def.name
+    ctx_registry[op] = ctx
     if not cls.registered:
-      @fw.tensorflow.RegisterGradient(name)
+      remap_in = cls.map_in_to_args(op, args)
+      remap_out = cls.map_res_to_out(op, result)
+      @fw.tensorflow.RegisterGradient(op.op_def.name)
       def gradient(op, *dy):
         # Remap gradient inputs in the right order
         dy = [dy[i] for i in remap_out]
         dy = dy if len(dy) > 1 else dy[0]
         # Execute gradient function
-        grad = cls.backward(ctx_registry[key], dy)
+        grad = cls.backward(ctx_registry[op], dy)
         grad = function.extract_tf_tensors(grad, 'backward')
         # Remap gradient in the right order
         ret = [None] * len(op.inputs)

python/triton/kernel.py

@@ -253,7 +253,7 @@ class kernel:
           bench_registry[y] = triton.utils.id_dict.lazy_entry(bench_id)
     elif fw.has_torch():
       args = [x.contiguous() if isinstance(x, fw.torch.Tensor) else x for x in args[:-1]]
-      self.fw_op(op_id, bench, bench_id, *args)
+      ret = self.fw_op(op_id, bench, bench_id, *args)
       if bench > 0:
         bench_registry[ret] = libtriton.retrieve_scalar(op_id)
     else:

python/triton/ops/__init__.py

@@ -1,3 +1,4 @@
 from .dot import _dot, dot
 from .einsum import _einsum, einsum
-from .batchnorm import _batchnorm, batchnorm
+from .batchnorm import _batchnorm, batchnorm
+from .conv import _conv, conv

python/triton/ops/conv.py

@@ -0,0 +1,234 @@
+import triton
+import numpy as np 
+
+class _conv(triton.function):
+
+  src = """
+void convnd(A_TYPE *A,
+          B_TYPE *B,
+          float *C,
+          int M, int N, int K,
+          int AH, int AW,
+          int BH, int BW,
+          int CH, int CW,
+          int NC,
+          int lda_n, int lda_c, int lda_d, int lda_h, int lda_w,
+          int ldb_c, int ldb_t, int ldb_r, int ldb_s, int ldb_k,
+          int ldc_n, int ldc_k, int ldc_m, int ldc_p, int ldc_q,
+          int pad_h, int pad_w,
+          int stride_h, int stride_w,
+          int upsample_h, int upsample_w,
+          int off_uh, int off_uw,
+          int off_uah, int off_uaw,
+          int off_uch, int off_ucw,
+          int* a_delta){
+
+  // range of indices along the reduction axis
+  int rka[TK] = 0 ... TK;
+  int rkb[TK] = 0 ... TK;
+
+  // initialize accumulator
+  float c[TM, TN] = 0;
+
+  // pointers for A
+  int rxa[TM] = get_program_id(0) * TM + 0 ... TM;
+  int rabh[TM] = rxa / CW;
+  int raw[TM] = rxa % CW;
+  int rab[TM] = rabh / CH;
+  int rah[TM] = rabh % CH;
+  rah = rah * UPAW - off_uah;
+  raw = raw * UPAH - off_uaw;
+  int racr[TK] = rka / BW;
+  int ras[TK] = rka % BW;
+  int rac[TK] = racr / BH;
+  int rar[TK] = racr % BH;
+  rar = FLIPR rar;
+  ras = FLIPS ras;
+  rar = UPAR * rar;
+  ras = UPAS * ras;
+  int ra0[TM] = rab*lda_n + rah*lda_h + raw*lda_w;
+  int ra1[TK] = rac*lda_c + rar*lda_h + ras*lda_w;
+  A_TYPE* pa[TM, TK] = A + ra0[:, newaxis] + ra1[newaxis, :];
+
+  // pointers for B
+  int rb0[TN] = get_program_id(1) * TN + 0 ... TN;
+#ifdef B_LUT
+  int rbcr[TK] = rkb / BW;
+  int rbs[TK] = rkb % BW;
+  int rbc[TK] = rbcr / BH;
+  int rbr[TK] = rbcr % BH;
+  rbr = rbr * upsample_h + off_uh;
+  rbs = rbs * upsample_w + off_uw;
+  int rb1[TK] = rbc*ldb_c + rbr*ldb_r + rbs*ldb_s;
+#else
+  int rb1[TK] = rkb * STRIDE_B0;
+#endif
+  B_TYPE* pb [B_SHAPE] = B + rb1[BROADCAST_B1] * STRIDE_B1 + rb0[BROADCAST_B0] * STRIDE_B0 * ldb_k;
+
+  // pointers for A look-up table
+  int offda[TK] = rka % LUT_SIZE;
+  int* pincd[TK] = a_delta + offda;
+  int* pda[TK]  = a_delta + LUT_SIZE + offda + off_uw * LUT_SIZE + off_uh * LUT_SIZE * upsample_w;
+  int da[TK] = *pda;
+  int incd[TK] = *pincd;
+
+  // pointers for B look-up table
+  int offdb[TK] = rkb % LUT_SIZE;
+#ifdef B_LUT
+  int* pdb[TK] = b_delta + offdb + off_uw * LUT_SIZE + off_uh * LUT_SIZE * upsample_w;
+  int db[TK] = *pdb;
+#endif
+
+  // reduction loop
+  A_TYPE a[TM, TK] = *pa;
+  B_TYPE b[B_SHAPE] = *pb;
+  for(int k = K; k > 0; k = k - TK){
+    c += a @ USE_B;
+    pa = pa + da[newaxis, :];
+    pb = pb + INC_PB;
+    // increment A look-up table
+    pda = pda + incd;
+    da = *pda;
+    pincd = pincd + incd;
+    incd = *pincd;
+    // increment B look-up table
+#ifdef B_LUT
+    pdb = pdb + INC_PDB;
+    db = *pdb;
+#endif
+    // pre-fetches
+    a = *pa;
+    b = *pb;
+  }
+
+  // write back
+  int rxc[TM] = get_program_id(0) * TM + 0 ... TM;
+  int rc1[TN] = get_program_id(1) * TN + 0 ... TN;
+  int rcn[TM] = rxc / (CH*CW);
+  int rcpq[TM] = rxc % (CH*CW);
+  int rcp[TM] = rcpq / CW;
+  int rcq[TM] = rcpq % CW;
+  rcp = rcp * upsample_h + off_uch;
+  rcq = rcq * upsample_w + off_ucw;
+  int rc0[TM] = rcn * ldc_n + rcp * ldc_p + rcq * ldc_q;
+  float* pc[TM, TN]  = C + rc1[newaxis, :]*ldc_k + rc0[:, newaxis];
+  *pc = c;
+}
+"""
+  kernel = triton.kernel(src, ['C'])
+
+  @staticmethod
+  def _unpack(idx, D, H, W):
+    c = idx // (D*H*W)
+    dhw = idx % (D*H*W)
+    dh = dhw // W
+    w = dhw % W
+    d = dh // H
+    h = dh % H
+    return c, d, h, w
+
+  @staticmethod
+  def _delta_a(upsample_d, upsample_h, upsample_w, depth, TK,
+               T, R, S, stride_a):
+    ud = np.arange(upsample_d)[:, np.newaxis, np.newaxis, np.newaxis]
+    uh = np.arange(upsample_h)[np.newaxis, :, np.newaxis, np.newaxis]
+    uw = np.arange(upsample_w)[np.newaxis, np.newaxis, :, np.newaxis]
+    ctrs = np.arange(depth)[np.newaxis, np.newaxis, np.newaxis, :]
+    c, t, r, s = _conv._unpack(ctrs, T, R, S)
+    nextc, nextt, nextr, nexts = _conv._unpack(ctrs + TK, T, R, S)
+    cdiff = nextc - c
+    tdiff = nextt - t
+    rdiff = nextr - r
+    sdiff = nexts - s
+    return cdiff*stride_a[1] + tdiff*stride_a[2] + rdiff*stride_a[3] + sdiff*stride_a[4]
+
+  @staticmethod
+  def _extract_strides(shape):
+    rank = len(shape)
+    ret = [1] * rank
+    for i in range(rank - 1, 0, -1):
+      ret[i-1] = ret[i] * shape[i]
+    return ret
+
+
+  @staticmethod
+  def _call(a, b, 
+            upsample_d, upsample_h, upsample_w, 
+            pad_d, pad_h, pad_w, 
+            stride_d, stride_h, stride_w, 
+            mode):
+    # input shapes
+    shape_a = list(triton.shape(a))
+    shape_b = list(triton.shape(b))
+    # add depth
+    shape_a.insert(2, 1)
+    shape_b.insert(1, 1)
+    NB, NC, AD, AH, AW = shape_a
+    NC, BD, BH, BW, NF = shape_b
+    # output shape
+    CD = (AD*upsample_d - BD + 1 + 2*pad_d + stride_d - 1) // stride_d
+    CH = (AH*upsample_h - BH + 1 + 2*pad_h + stride_h - 1) // stride_h
+    CW = (AW*upsample_w - BW + 1 + 2*pad_w + stride_w - 1) // stride_w
+    shape_c = [NB, NF, CD, CH, CW]
+    # strides
+    stride_a = _conv._extract_strides(shape_a)
+    stride_b = _conv._extract_strides(shape_b)
+    stride_c = _conv._extract_strides(shape_c)
+    # look-up tables
+    TK = 8
+    FS = BD * BH * BW 
+    depth = (TK + FS - 1)//FS * FS
+    delta_a = _conv._delta_a(upsample_d, upsample_h, upsample_w, 
+                             depth, TK, BD, BH, BW, stride_a)
+    delta_a = triton.fw.torch.from_numpy(delta_a).cuda()
+
+    trans_b = False
+    is_wgrad = False
+    is_blut = False 
+    macros = { 
+               'B_SHAPE':      'TN, TK'       if trans_b else 'TK, TN',
+               'BROADCAST_B0': ':, newaxis'   if trans_b else 'newaxis, :',
+               'BROADCAST_B1': 'newaxis, :'   if trans_b else ':, newaxis',
+               'STRIDE_B0':    'ldb_s'          if trans_b else '1',
+               'STRIDE_B1':    '1'              if trans_b else 'ldb_s',
+               'USE_B':        '^b'             if trans_b else 'b',
+               'FLIPR':        ''               if trans_b else 'BH - 1 -',
+               'FLIPS':        ''               if trans_b else 'BW - 1 -',
+               'UPAR':         'stride_h'       if is_wgrad else '1',
+               'UPAS':         'stride_w'       if is_wgrad else '1',
+               'UPAH':         ''               if is_wgrad else 'stride_h',
+               'UPAW':         ''               if is_wgrad else 'stride_w',
+               'REDAX0':       'NC'             if trans_b else 'BH',
+               'REDAX1':       'BH'             if trans_b else 'BW',
+               'REDAX2':       'BW'             if trans_b else 'NC',
+               'AX0':          'c'              if trans_b else 'r',
+               'AX1':          'r'              if trans_b else 's',
+               'AX2':          's'              if trans_b else 'c',
+               'INC_PB':       'db[newaxis, :]' if is_blut else 'TK',
+               'INC_PDB':      'incd'           if trans_b else 'TK',
+               'LUT_SIZE':      depth,
+               'TM': [32],
+               'TN': [32],
+               'TK': TK,
+               'A_TYPE': 'float',
+               'B_TYPE': 'float'
+              }
+    
+    shape_c.pop(2)
+    print(shape_c)
+    c = triton.empty(shape_c, dtype=a.dtype)
+    _conv.kernel(a, b, c, CD*CH*CW, NF, NC*BD*BH*BW, AH, AW, BH, BW, CH, CW, NC,
+                 stride_a[0], stride_a[1], stride_a[2], stride_a[3], stride_a[4],
+                 stride_b[0], stride_b[1], stride_b[2], stride_b[3], stride_b[4],
+                 stride_c[0], stride_c[1], stride_c[2], stride_c[3], stride_c[4],
+                 pad_h, pad_w, stride_h, stride_w, upsample_h, upsample_w, 
+                 0, 0, 0, 0, 0, 0,
+                 delta_a,
+                 lambda opt: (1, 1, 1), **macros)
+    return c
+  
+  @staticmethod
+  def forward(ctx, input, weight):
+    _conv._call(input, weight, 1, 1, 1, 0, 0, 0, 1, 1, 1, '')
+
+conv = _conv.apply

python/triton/ops/dot.py

@@ -36,7 +36,6 @@ void dot(TYPE * A, TYPE * B, TYPE * C,
   *pc = c;
 }
 """
-
   kernel = triton.kernel(src, ['C'])
 
   @staticmethod
@@ -109,6 +108,6 @@ void dot(TYPE * A, TYPE * B, TYPE * C,
       db = _dot._call(dy, a, True, True, bench)
     else:
       assert False
-    return da, db, None, None, None, None, None, None, None
+    return da, db, None, None, None
 
 dot = _dot.apply

python/triton/utils.py

@@ -1,6 +1,7 @@
 import triton.frameworks as fw
 import triton._C.libtriton as libtriton
 import numpy as np
+import weakref
 
 def cdiv(a, b):
     return -(-a // b)
@@ -23,7 +24,7 @@ def empty(shape, dtype):
     return tf_empty_proxy(shape, dtype)
     #return fw.tf_extra_ops.alloc_empty(args, T = dtype)
   elif fw.has_torch():
-    return fw.torch.empty(*shape).cuda()
+    return fw.torch.empty(shape).cuda()
 
 def shape(A) :
   if fw.has_tensorflow():
@@ -45,15 +46,17 @@ class id_dict:
     def get(self):
       return libtriton.retrieve_scalar(self.id)
 
-
   def __init__(self):
-    self.data = dict()
+    self.data = weakref.WeakKeyDictionary()
 
   def __delitem__(self, key):
-    del self.data[id(key)]
+    del self.data[key]
 
   def __getitem__(self, key):
-    ret = self.data[id(key)]
+    if fw.has_tensorflow():
+      if isinstance(key, fw.tensorflow.Tensor):
+        key = key.op
+    ret = self.data[key]
     if isinstance(ret, id_dict.lazy_entry):
       return ret.get()
     return ret
@@ -62,4 +65,7 @@ class id_dict:
     return len(self.data)
 
   def __setitem__(self, key, value):
-    self.data[id(key)] = value
+    if fw.has_tensorflow():
+      if isinstance(key, fw.tensorflow.Tensor):
+        key = key.op
+    self.data[key] = value