Fixed bugs for conv2d (#1465)

topi/python/topi/cuda/conv2d_nchw.py

@@ -13,14 +13,16 @@ def conv2d_224_3_64(s, temp, temp_R, temp_S, Filter_S, Out, Out_L, flag):
     hfactor = 2
     if flag >= 96:
         hfactor = 4
+    max_threads = int(tvm.target.current_target(allow_none=False).max_num_threads)
     ow_size = util.get_const_int(Out.shape[3])
-    num_thread = ow_size * hfactor
+    num_thread = min(max_threads, ow_size * hfactor)
     vthread = ofactor
     block_x = tvm.thread_axis("blockIdx.x")
     thread_x = tvm.thread_axis((0, num_thread), "threadIdx.x")
     thread_xz = tvm.thread_axis((0, vthread), "vthread", name="vx")
 
     i, oc, h, w = s[Out].op.axis
+    if ow_size * hfactor == num_thread:
         ooc, ioc = s[Out].split(oc, factor=vthread)
         oh, ih = s[Out].split(h, factor=hfactor)
         s[Out].reorder(ooc, oh, ioc, ih, w)
@@ -30,6 +32,10 @@ def conv2d_224_3_64(s, temp, temp_R, temp_S, Filter_S, Out, Out_L, flag):
         s[Out].bind(w, thread_x)
         s[Out].bind(ioc, thread_xz)
         s[Out].bind(oc, block_x)
+    else:
+        ow, w = s[Out].split(w, factor=num_thread)
+        s[Out].bind(w, thread_x)
+        s[Out].bind(ow, block_x)
 
     s[Out_L].compute_at(s[Out], w)
 
@@ -40,7 +46,7 @@ def conv2d_224_3_64(s, temp, temp_R, temp_S, Filter_S, Out, Out_L, flag):
     s[temp_S].compute_at(s[Out_L], ic)
     s[Filter_S].compute_at(s[Out_L], w)
 
-    num_thread1 = tvm.target.current_target(allow_none=False).max_num_threads
+    num_thread1 = max_threads
     thread_xx = tvm.thread_axis((0, num_thread1), "threadIdx.x")
     block_xx = tvm.thread_axis("blockIdx.x")
 
@@ -59,6 +65,7 @@ def conv2d_224_3_64(s, temp, temp_R, temp_S, Filter_S, Out, Out_L, flag):
     h = s[temp_S].fuse(h, ow)
     _, tx = s[temp_S].split(h, factor=num_thread)
     s[temp_S].bind(tx, thread_x)
+    if num_thread < max_threads:
         s[temp_S].vectorize(iw)
 
     #schedule Filter_S shared mem load
@@ -250,12 +257,13 @@ def conv2d_56_64_128(s, temp, temp_R, temp_S, Filter_S, Out, Out_L, flag):
 
 def conv2d_14_256_256(s, temp, temp_R, temp_S, Filter, Filter_S, Out, Out_L):
     """Schedule conv2d for specific feature_in_out_filter pattern"""
+    max_threads = int(tvm.target.current_target(allow_none=False).max_num_threads)
     if util.get_const_int(Filter.shape[0]) + util.get_const_int(Filter.shape[1]) <= 768:
         # scheduler params
         vthread_x = util.get_const_int(Out.shape[3])
         num_thread_x = 64
         ofactor = 8
-        if util.get_const_int(Filter.shape[3]) == 1:
+        if util.get_const_int(Filter.shape[3]) == 1 and vthread_x * 5 <= max_threads:
             ofactor = 64
         block_x = tvm.thread_axis("blockIdx.x")
         thread_x = tvm.thread_axis((0, num_thread_x), "threadIdx.x")
@@ -295,9 +303,9 @@ def conv2d_14_256_256(s, temp, temp_R, temp_S, Filter, Filter_S, Out, Out_L):
 
     else:
         # scheduler params
-        vthread_x = util.get_const_int(Out.shape[2])
+        vthread_x = min(8, util.get_const_int(Out.shape[2]))
         num_thread_x = 16
-        num_thread_y = util.get_const_int(Out.shape[3])
+        num_thread_y = min(max_threads // num_thread_x, util.get_const_int(Out.shape[3]))
         ofactor = 8
         block_x = tvm.thread_axis("blockIdx.x")
         thread_x = tvm.thread_axis((0, num_thread_x), "threadIdx.x")
@@ -305,11 +313,13 @@ def conv2d_14_256_256(s, temp, temp_R, temp_S, Filter, Filter_S, Out, Out_L):
         thread_xz = tvm.thread_axis((0, vthread_x), "vthread", name="vx")
 
         i, oc, h, w = s[Out].op.axis
+        ow, iw = s[Out].split(w, factor=num_thread_y)
+        oh, ih = s[Out].split(h, factor=vthread_x)
         ooc, ioc = s[Out].split(oc, factor=num_thread_x)
-        s[Out].reorder(i, ooc, h, w, ioc)
+        s[Out].reorder(i, ooc, oh, ih, ow, iw, ioc)
         s[Out].bind(ioc, thread_x)
-        s[Out].bind(w, thread_y)
-        s[Out].bind(h, thread_xz)
+        s[Out].bind(iw, thread_y)
+        s[Out].bind(ih, thread_xz)
         s[Out].bind(ooc, block_x)
 
         s[Out_L].compute_at(s[Out], ioc)
@@ -323,7 +333,7 @@ def conv2d_14_256_256(s, temp, temp_R, temp_S, Filter, Filter_S, Out, Out_L):
         s[temp_S].compute_at(s[Out_L], oic)
         s[Filter_S].compute_at(s[Out_L], oic)
 
-        num_thread = tvm.target.current_target(allow_none=False).max_num_threads
+        num_thread = max_threads
         thread_xx = tvm.thread_axis((0, num_thread), "threadIdx.x")
         block_xx = tvm.thread_axis("blockIdx.x")
 

topi/python/topi/intel_graphics/conv2d.py

-# pylint: disable=invalid-name,unused-variable,unused-argument,no-else-return
+# pylint: disable=invalid-name,unused-variable,unused-argument,no-else-return, too-many-arguments, too-many-locals, too-many-statements, no-member, too-many-branches
 """conv2d schedule on Intel Graphics"""
 
 from __future__ import absolute_import as _abs
@@ -57,7 +57,8 @@ def _alter_conv2d_layout(attrs, inputs, tinfos):
     return sym.contrib.conv2d_NCHWc(*copy_inputs, **new_attrs)
 
 @conv2d_NCHWc.register(["intel_graphics"])
-def _decl_conv2d(data, kernel, num_filter, kernel_size, stride, padding, out_dtype='float32'):
+def _decl_conv2d(data, kernel, num_filter, kernel_size, stride, padding, layout,\
+                 out_layout, out_dtype='float32'):
     """Conv2D operator for Intel Graphics backend.
 
     Parameters
@@ -96,7 +97,7 @@ def _decl_conv2d(data, kernel, num_filter, kernel_size, stride, padding, out_dty
     return _decl_cl_spatialpack_NCHWc(data, kernel, stride, padding, out_dtype)
 
 @generic.schedule_conv2d_NCHWc.register(["intel_graphics"])
-def schedule_conv2d_NCHWc(num_filter, kernel_size, stride, padding, outs):
+def schedule_conv2d_NCHWc(num_filter, kernel_size, stride, padding, layout, out_layout, outs):
     """Schedule for conv2d_nchw for Intel Graphics
 
     Parameters

topi/tests/python/test_topi_conv2d_nchw.py

@@ -74,7 +74,7 @@ def test_conv2d_nchw():
     verify_conv2d_nchw(1, 256, 14, 512, 3, 2, 1)
     verify_conv2d_nchw(1, 256, 14, 512, 1, 2, 0)
     verify_conv2d_nchw(1, 512, 7, 512, 3, 1, 1)
-    # ResNet 50 workloads
+    # ResNet50 workloads
     verify_conv2d_nchw(1, 64, 56, 256, 1, 1, 0)
     verify_conv2d_nchw(1, 256, 56, 64, 1, 1, 0)
     verify_conv2d_nchw(1, 256, 56, 128, 1, 2, 0)