From 16791b034e98daebbba33e13f15086237b6961fd Mon Sep 17 00:00:00 2001
From: Andreas Kloeckner <inform@tiker.net>
Date: Tue, 16 Jul 2013 23:30:00 -0400
Subject: [PATCH] Add prefetch to convolution demo
---
test/test_loopy.py | 81 +++++++++++++++++++++++++++++++++++++++++++---
1 file changed, 77 insertions(+), 4 deletions(-)
diff --git a/test/test_loopy.py b/test/test_loopy.py
index d425bf94f..6934aad00 100644
--- a/test/test_loopy.py
+++ b/test/test_loopy.py
@@ -1148,10 +1148,78 @@ def test_vector_ilp_with_prefetch(ctx_factory):
assert len(list(re.finditer("barrier", code))) == 1
+# {{{ convolutions
+
def test_convolution(ctx_factory):
ctx = ctx_factory()
- dtype = np.float64
+ dtype = np.float32
+
+ knl = lp.make_kernel(ctx.devices[0],
+ "{ [iimg, ifeat, icolor, im_x, im_y, f_x, f_y]: \
+ -f_w <= f_x,f_y <= f_w \
+ and 0 <= im_x < im_w and 0 <= im_y < im_h \
+ and 0<=iimg<=nimgs and 0<=ifeat<nfeats and 0<=icolor<ncolors \
+ }",
+ """
+ out[iimg, ifeat, im_x, im_y] = sum((f_x, f_y, icolor), \
+ img[iimg, f_w+im_x-f_x, f_w+im_y-f_y, icolor] \
+ * f[ifeat, f_w+f_x, f_w+f_y, icolor])
+ """,
+ [
+ lp.GlobalArg("f", dtype, shape=lp.auto),
+ lp.GlobalArg("img", dtype, shape=lp.auto),
+ lp.GlobalArg("out", dtype, shape=lp.auto),
+ "..."
+ ],
+ assumptions="f_w>=1 and im_w, im_h >= 2*f_w+1 and nfeats>=1 and nimgs>=0",
+ flags="annotate_inames",
+ defines=dict(ncolors=3))
+
+ ref_knl = knl
+
+ f_w = 3
+
+ def variant_0(knl):
+ #knl = lp.split_iname(knl, "im_x", 16, inner_tag="l.0")
+ knl = lp.set_loop_priority(knl, "iimg,im_x,im_y,ifeat,f_x,f_y")
+ return knl
+
+ def variant_1(knl):
+ knl = lp.split_iname(knl, "im_x", 16, inner_tag="l.0")
+ return knl
+
+ def variant_2(knl):
+ knl = lp.split_iname(knl, "im_x", 16, outer_tag="g.0", inner_tag="l.0")
+ knl = lp.split_iname(knl, "im_y", 16, outer_tag="g.1", inner_tag="l.1")
+ knl = lp.fix_parameters(knl, f_w=f_w)
+ knl = lp.tag_inames(knl, dict(ifeat="g.2"))
+ knl = lp.add_prefetch(knl, "f[ifeat,:,:,:]")
+ knl = lp.add_prefetch(knl, "img", "im_x_inner, im_y_inner, f_x, f_y")
+ return knl
+
+ for variant in [
+ #variant_0,
+ #variant_1,
+ variant_2
+ ]:
+ lp.auto_test_vs_ref(ref_knl, ctx, variant(knl),
+ parameters=dict(
+ im_w=128, im_h=128, f_w=f_w,
+ nfeats=12, nimgs=17
+ ))
+
+
+def test_convolution_with_nonzero_base(ctx_factory):
+ # This is kept alive as a test for domains that don't start at zero.
+ # These are a bad idea for split_iname, which places its origin at zero
+ # and therefore produces a first block that is odd-sized.
+ #
+ # Therefore, for real tests, check test_convolution further up.
+
+ ctx = ctx_factory()
+
+ dtype = np.float32
knl = lp.make_kernel(ctx.devices[0],
"{ [iimg, ifeat, icolor, im_x, im_y, f_x, f_y]: \
@@ -1170,11 +1238,14 @@ def test_convolution(ctx_factory):
lp.GlobalArg("out", dtype, shape=lp.auto),
"..."
],
- assumptions="f_w>=1 and im_w, im_h >= 2*f_w+1 and nfeats>=1",
+ assumptions="f_w>=1 and im_w, im_h >= 2*f_w+1 and nfeats>=1 and nimgs>=0",
+ flags="annotate_inames",
defines=dict(ncolors=3))
ref_knl = knl
+ f_w = 3
+
def variant_0(knl):
#knl = lp.split_iname(knl, "im_x", 16, inner_tag="l.0")
knl = lp.set_loop_priority(knl, "iimg,im_x,im_y,ifeat,f_x,f_y")
@@ -1186,14 +1257,16 @@ def test_convolution(ctx_factory):
for variant in [
variant_0,
- #variant_1
+ variant_1,
]:
lp.auto_test_vs_ref(ref_knl, ctx, variant(knl),
parameters=dict(
- im_w=128, im_h=128, f_w=7,
+ im_w=128, im_h=128, f_w=f_w,
nfeats=12, nimgs=17
))
+# }}}
+
def test_c_instruction(ctx_factory):
#logging.basicConfig(level=logging.DEBUG)
--
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