ekalda commented on code in PR #13669:
URL: https://github.com/apache/tvm/pull/13669#discussion_r1058905667
##########
tests/python/topi/python/test_topi_conv2d_int8.py:
##########
@@ -298,378 +175,462 @@ def get_ref_data():
a_np, w_np, b_np, c_np = get_ref_data()
- with tvm.target.Target(target):
- C = compute(
- A,
- W,
- (stride, stride),
- padding,
- (dilation, dilation),
- "NCHW",
- "NCHW",
- out_dtype,
- )
+ dev = tvm.device(target, 0)
+ with tvm.target.Target(target) as tvm_target:
+ C = compute(A, W, (stride, stride), padding, (dilation, dilation),
dtype)
if add_bias:
C = topi.add(C, bias)
if add_relu:
C = topi.nn.relu(C)
s = schedule([C])
- a = tvm.nd.array(a_np.astype(dtype), dev)
- w = tvm.nd.array(w_np.astype(dtype), dev)
- b = tvm.nd.array(b_np.astype(out_dtype), dev)
- c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype),
dev)
-
- if add_bias:
- compile_args = [A, W, bias, C]
- run_args = [a, w, b, c]
- else:
- compile_args = [A, W, C]
- run_args = [a, w, c]
-
- func = tvm.build(
- s,
- compile_args,
- target,
- name="relu_%d_%d_%d_%d_%d_%d_%d_%d"
- % (batch, in_channel, in_size, num_filter, kernel, stride,
padding_sum, dilation),
- )
+ a = tvm.nd.array(a_np, dev)
+ w = tvm.nd.array(w_np, dev)
+ b = tvm.nd.array(b_np, dev)
+ c = tvm.nd.array(np.zeros(get_const_tuple(C.shape),
dtype=C.dtype), dev)
- if build_only:
- return
+ build_inputs = [A, W, bias, C] if add_bias else [A, W, C]
+ inference_inputs = (a, w, b, c) if add_bias else (a, w, c)
+
+ func = tvm.build(
+ s,
+ build_inputs,
+ target,
+ name="relu_%d_%d_%d_%d_%d_%d_%d_%d"
+ % (
+ batch,
+ in_channel,
+ in_size,
+ num_filter,
+ kernel,
+ stride,
+ padding_sum,
+ dilation,
+ ),
+ )
- print("Running on target: %s" % target)
+ build_only = tvm_target.features.is_aarch64 and
(platform.machine() != "aarch64")
- func(*run_args)
+ if not build_only:
+ print("Running on target: %s" % target)
+ func(*inference_inputs)
+ tvm.testing.assert_allclose(c.numpy(), c_np, rtol=1e-5)
- tvm.testing.assert_allclose(c.numpy(), c_np, rtol=1e-5)
- targets = [
- (
- "cuda",
- lambda a, w, s, p, d, l, ol, o: topi.cuda.conv2d_NCHWc_int8(a, w,
s, p, d, l, o),
- topi.cuda.schedule_conv2d_NCHWc_int8,
- 4,
- False,
- ),
- # Disable on CI since it does not support spirv int8 dot product
- # (
- # "vulkan -from_device=0",
- # lambda a, w, s, p, d, l, ol, o: topi.cuda.conv2d_NCHWc_int8(a,
w, s, p, d, l, o),
- # topi.cuda.schedule_conv2d_NCHWc_int8,
- # 4,
- # False,
- # ),
- ]
-
- build_only_aarch64 = platform.machine() != "aarch64"
-
- targets.append(
[email protected]("in_dtype", ["int8", "uint8"])
[email protected](
+ "params",
+ [
+ # ResNet18 workloads where channels in / out are multiple of
oc_block_factor
+ (1, 64, 56, 64, 3, 1, 1, 1, False, False),
+ (1, 64, 56, 64, 1, 1, 0, 1, False, False),
+ (1, 64, 56, 128, 3, 2, 1, 1, False, False),
+ (1, 64, 56, 128, 1, 2, 0, 1, False, False),
+ (1, 128, 28, 128, 3, 1, 1, 1, False, False),
+ (1, 128, 28, 256, 3, 2, 1, 1, False, False),
+ (1, 128, 28, 256, 1, 2, 0, 1, False, False),
+ (1, 256, 14, 256, 3, 1, 1, 1, False, False),
+ (1, 256, 14, 512, 3, 2, 1, 1, False, False),
+ (1, 256, 14, 512, 1, 2, 0, 1, False, False),
+ (1, 512, 7, 512, 3, 1, 1, 1, False, False),
+ # bias, relu
+ (1, 64, 56, 64, 3, 1, 1, 1, False, True),
+ (1, 64, 56, 64, 3, 1, 1, 1, True, False),
+ (1, 64, 56, 64, 3, 1, 1, 1, True, True),
+ # dilation = 2
+ (1, 64, 56, 64, 3, 1, 1, 2, False, False),
+ # batch size
+ (4, 64, 56, 64, 3, 1, 1, 1, False, False),
+ (9, 64, 56, 64, 3, 1, 1, 1, False, False),
+ # weird workloads
+ (4, 4, 4, 8, 4, 4, 4, 1, False, False),
+ # inception v3 workloads where channels in / out are multiple of
oc_block_factor
+ (1, 32, 149, 32, 3, 1, 0, 1, False, False),
+ (1, 32, 147, 64, 3, 1, 1, 1, False, False),
+ (1, 64, 73, 80, 1, 1, 0, 1, False, False),
+ (1, 80, 73, 192, 3, 1, 0, 1, False, False),
+ (1, 192, 35, 64, 1, 1, 0, 1, False, False),
+ (1, 192, 35, 48, 1, 1, 0, 1, False, False),
+ (1, 48, 35, 64, 5, 1, 2, 1, False, False),
+ (1, 64, 35, 96, 3, 1, 1, 1, False, False),
+ (1, 96, 35, 96, 3, 1, 1, 1, False, False),
+ (1, 192, 35, 32, 1, 1, 0, 1, False, False),
+ (1, 256, 35, 64, 1, 1, 0, 1, False, False),
+ (1, 256, 35, 48, 1, 1, 0, 1, False, False),
+ (1, 288, 35, 64, 1, 1, 0, 1, False, False),
+ (1, 288, 35, 48, 1, 1, 0, 1, False, False),
+ (1, 288, 35, 384, 3, 2, 0, 1, False, False),
+ (1, 96, 35, 96, 3, 2, 0, 1, False, False),
+ (1, 768, 17, 192, 1, 1, 0, 1, False, False),
+ (1, 768, 17, 128, 1, 1, 0, 1, False, False),
+ (1, 128, 17, 128, 1, 1, 0, 1, False, False),
+ (1, 128, 17, 192, 7, 1, 3, 1, False, False),
+ (1, 128, 17, 128, 7, 1, 3, 1, False, False),
+ (1, 128, 17, 192, 1, 1, 0, 1, False, False),
+ (1, 768, 17, 160, 1, 1, 0, 1, False, False),
+ (1, 160, 17, 160, 1, 1, 0, 1, False, False),
+ (1, 160, 17, 192, 7, 1, 3, 1, False, False),
+ (1, 160, 17, 160, 7, 1, 3, 1, False, False),
+ (1, 160, 17, 192, 1, 1, 0, 1, False, False),
+ (1, 192, 17, 192, 1, 1, 0, 1, False, False),
+ (1, 192, 17, 192, 7, 1, 3, 1, False, False),
+ (1, 192, 17, 320, 3, 2, 0, 1, False, False),
+ (1, 192, 17, 192, 3, 2, 0, 1, False, False),
+ (1, 1280, 8, 320, 1, 1, 0, 1, False, False),
+ (1, 1280, 8, 384, 1, 1, 0, 1, False, False),
+ (1, 384, 8, 384, 1, 1, 0, 1, False, False),
+ (1, 384, 8, 384, 3, 1, 1, 1, False, False),
+ (1, 1280, 8, 448, 1, 1, 0, 1, False, False),
+ (1, 448, 8, 384, 3, 1, 1, 1, False, False),
+ (1, 1280, 8, 192, 1, 1, 0, 1, False, False),
+ (1, 2048, 8, 320, 1, 1, 0, 1, False, False),
+ (1, 2048, 8, 384, 1, 1, 0, 1, False, False),
+ (1, 2048, 8, 448, 1, 1, 0, 1, False, False),
+ (1, 2048, 8, 192, 1, 1, 0, 1, False, False),
+ (1, 1024, 19, 88, 3, 1, 1, 1, False, False),
+ # batch > 1
+ (7, 32, 149, 32, 3, 1, 0, 1, False, False),
+ (8, 32, 149, 32, 3, 1, 0, 1, False, False),
+ (32, 32, 149, 32, 3, 1, 0, 1, False, False),
+ # Asymmetric padding
+ (1, 32, 35, 64, 7, 2, (0, 0, 1, 1), 1, False, False),
+ (1, 64, 8, 128, 3, 1, (3, 3, 2, 2), 1, False, False),
+ (1, 64, 8, 64, 1, 1, (1, 2, 2, 1), 1, False, False),
+ (1, 64, 17, 192, 1, 1, (1, 2), 1, False, False),
+ (1, 64, 8, 64, 3, 1, (3, 1), 1, False, False),
+ (1, 128, 8, 384, 3, 1, (0, 2), 1, False, False),
+ (1, 64, 8, 64, 1, 1, "VALID", 1, False, False),
+ (1, 392, 8, 64, 3, 1, "VALID", 1, False, False),
+ (1, 512, 19, 64, 1, 1, "SAME", 1, False, False),
+ (1, 64, 16, 32, 2, 1, "SAME", 1, False, False),
+ (1, 64, 8, 64, 3, 1, (1, 2, 2, 1), 1, False, True),
+ (1, 64, 8, 64, 5, 2, (1, 3), 1, True, False),
+ (1, 64, 56, 64, 3, 1, "VALID", 1, True, True),
+ (1, 64, 56, 64, 24, 1, "SAME", 1, True, True),
+ ],
+)
+def test_conv2d_NCHWc_int8(in_dtype, params):
+ with Int8Fallback():
(
- "llvm -device arm_cpu -mtriple aarch64-linux-gnu
-mattr=+neon,+v8.2a,+dotprod",
- topi.arm_cpu.conv2d_NCHWc_int8,
- topi.arm_cpu.schedule_conv2d_NCHWc_int8,
- 8,
- build_only_aarch64,
+ batch,
+ in_channel,
+ in_size,
+ num_filter,
+ kernel,
+ stride,
+ padding,
+ dilation,
+ add_bias,
+ add_relu,
+ ) = params
+ pad_top, pad_left, pad_bottom, pad_right = get_pad_tuple(padding,
(kernel, kernel))
+ padding_sum = pad_top + pad_left + pad_bottom + pad_right
+ print(
+ "Workload: (%d, %d, %d, %d, %d, %d, %d, %d)"
+ % (batch, in_channel, in_size, num_filter, kernel, stride,
padding_sum, dilation)
)
- )
-
- if in_dtype == "int8":
- targets += [
- (
- "llvm -device arm_cpu -mtriple aarch64-linux-gnu -mattr=+neon",
- topi.arm_cpu.conv2d_NCHWc_int8,
- topi.arm_cpu.schedule_conv2d_NCHWc_int8,
- 8,
- build_only_aarch64,
- ),
- (
- "rocm -mattr=+dotprod",
- lambda a, w, s, p, d, l, ol, o: topi.cuda.conv2d_NCHWc_int8(a,
w, s, p, d, l, o),
- topi.cuda.schedule_conv2d_NCHWc_int8,
- 4,
- False,
- ),
- ]
-
- for target, compute, schedule, oc_block_factor, build_only in targets:
- check_target(target, compute, schedule, oc_block_factor, build_only)
-
-
-def verify_conv2d_nchw_int8(
- in_dtype,
- batch,
- in_channel,
- in_size,
- num_filter,
- kernel,
- stride,
- padding,
- dilation=1,
- add_bias=False,
- add_relu=False,
-):
- pad_top, pad_left, pad_bottom, pad_right = get_pad_tuple(padding, (kernel,
kernel))
- padding_sum = pad_top + pad_left + pad_bottom + pad_right
- print(
- "Workload: (%d, %d, %d, %d, %d, %d, %d, %d)"
- % (batch, in_channel, in_size, num_filter, kernel, stride,
padding_sum, dilation)
- )
-
- in_height = in_width = in_size
-
- A = te.placeholder((batch, in_channel, in_height, in_width), name="A",
dtype=in_dtype)
- W = te.placeholder((num_filter, in_channel, kernel, kernel), name="W",
dtype=in_dtype)
- bias = te.placeholder((num_filter, 1, 1), name="bias", dtype=in_dtype)
-
- a_shape = get_const_tuple(A.shape)
- w_shape = get_const_tuple(W.shape)
- bias_shape = get_const_tuple(bias.shape)
- dtype = A.dtype
-
- @memoize("topi.tests.test_topi_conv2d_int8.verify_conv2d_nchw")
- def get_ref_data():
- a_np = np.random.randint(low=-128, high=127,
size=a_shape).astype(dtype)
- w_np = np.random.randint(low=-128, high=128,
size=w_shape).astype(dtype)
- b_np = np.random.uniform(size=bias_shape).astype(dtype)
- dw_np = tvm.topi.testing.dilate_python(w_np, (1, 1, dilation,
dilation))
- c_np = tvm.topi.testing.conv2d_nchw_python(a_np, dw_np, stride,
padding).astype(dtype)
-
- if add_bias:
- b_np = np.random.uniform(size=bias_shape).astype(dtype)
- c_np += b_np
- if add_relu:
- c_np = np.maximum(c_np, 0)
-
- return a_np, w_np, b_np, c_np
-
- a_np, w_np, b_np, c_np = get_ref_data()
-
- def verify_workload_padding():
- _, _, out_height, out_width = get_const_tuple(c_np.shape)
- wkl = _get_workload(A, W, (stride, stride), padding, dilation, dtype)
-
- # for testing functionality,
- # we choose arbitrary int32_lanes and num_int8_elements can divide the
channel,
- # regardless of the performance.
- int32_lanes, num_int8_elements = num_filter, in_channel
- # check if tile_ow candidates are the factors of the right output
weight.
- cfg = autotvm.get_config()
- fallback_schedule_cpu_common_int8(cfg, wkl, int32_lanes,
num_int8_elements)
- ow_tile = np.prod(cfg["tile_ow"].size)
-
- tvm.testing.assert_allclose(ow_tile, out_width)
+ in_height = in_width = in_size
+
+ A = te.placeholder((batch, in_channel, in_height, in_width), name="A",
dtype=in_dtype)
+ W = te.placeholder((num_filter, in_channel, kernel, kernel), name="W",
dtype=in_dtype)
+
+ a_shape = get_const_tuple(A.shape)
+ w_shape = get_const_tuple(W.shape)
+ dtype = A.dtype
+ out_dtype = "int32" if in_dtype == "int8" else "uint32"
+ lo = -128 if in_dtype == "int8" else 0
+ hi = 127 if in_dtype == "int8" else 255
+
+ def check_target(target, compute, schedule, oc_block_factor,
build_only):
+ dev = tvm.device(target, 0)
+ if not tvm.testing.device_enabled(target):
+ print("Skip because %s is not enabled" % target)
+ return
+ if target == "cuda" and not
tvm.contrib.nvcc.have_int8(dev.compute_version):
+ print("Skip because int8 intrinsics are not available")
+ return
+
+ bias = te.placeholder(
+ (num_filter // oc_block_factor, 1, 1, oc_block_factor),
name="bias", dtype=out_dtype
+ )
+ bias_shape = get_const_tuple(bias.shape)
- def check_target(target):
- dev = tvm.device(target, 0)
- if not tvm.testing.device_enabled(target):
- print("Skip because %s is not enabled" % target)
- return
- if target == "cuda" and not
tvm.contrib.nvcc.have_int8(dev.compute_version):
- print("Skip because int8 intrinsics are not available")
- return
+ @memoize("topi.tests.test_topi_conv2d_int8.verify_conv2d_nchw")
+ def get_ref_data():
+ a_np = np.random.randint(low=lo, high=hi,
size=a_shape).astype(out_dtype)
+ w_np = np.random.randint(low=lo, high=hi,
size=w_shape).astype(out_dtype)
+ b_np = np.random.uniform(size=bias_shape).astype(out_dtype)
+ dw_np = tvm.topi.testing.dilate_python(w_np, (1, 1, dilation,
dilation))
+ c_np = tvm.topi.testing.conv2d_nchw_python(a_np, dw_np,
stride, padding).astype(
+ out_dtype
+ )
+
+ # convert to NCHWc
+ _, _, out_height, out_width = c_np.shape
+ c_np = c_np.reshape(
+ (batch, num_filter // oc_block_factor, oc_block_factor,
out_height, out_width)
+ ).transpose(0, 1, 3, 4, 2)
+
+ if add_bias:
+ b_np = np.random.uniform(size=bias_shape).astype(out_dtype)
+ c_np += b_np
+ if add_relu:
+ c_np = np.maximum(c_np, 0)
+
+ return a_np, w_np, b_np, c_np
+
+ a_np, w_np, b_np, c_np = get_ref_data()
+
+ with tvm.target.Target(target):
+ C = compute(
+ A,
+ W,
+ (stride, stride),
+ padding,
+ (dilation, dilation),
+ "NCHW",
+ "NCHW",
+ out_dtype,
+ )
+ if add_bias:
+ C = topi.add(C, bias)
+ if add_relu:
+ C = topi.nn.relu(C)
+ s = schedule([C])
+
+ a = tvm.nd.array(a_np.astype(dtype), dev)
+ w = tvm.nd.array(w_np.astype(dtype), dev)
+ b = tvm.nd.array(b_np.astype(out_dtype), dev)
+ c = tvm.nd.array(np.zeros(get_const_tuple(C.shape),
dtype=C.dtype), dev)
- print("Running on target: %s" % target)
- with tvm.target.Target(target):
- C = topi.cuda.conv2d_nchw_int8(
- A, W, (stride, stride), padding, (dilation, dilation), dtype
- )
if add_bias:
- C = topi.add(C, bias)
- if add_relu:
- C = topi.nn.relu(C)
- s = topi.cuda.schedule_conv2d_nchw_int8([C])
-
- a = tvm.nd.array(a_np, dev)
- w = tvm.nd.array(w_np, dev)
- b = tvm.nd.array(b_np, dev)
- c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype),
dev)
- if add_bias:
- tvm.build(
- s,
- [A, W, bias, C],
- target,
- name="relu_%d_%d_%d_%d_%d_%d_%d_%d"
- % (batch, in_channel, in_size, num_filter, kernel, stride,
padding_sum, dilation),
- )
- func = tvm.build(
- s,
- [A, W, bias, C],
- target,
- name="relu_%d_%d_%d_%d_%d_%d_%d_%d"
- % (batch, in_channel, in_size, num_filter, kernel, stride,
padding_sum, dilation),
- )
- func(a, w, b, c)
- else:
+ compile_args = [A, W, bias, C]
+ run_args = [a, w, b, c]
+ else:
+ compile_args = [A, W, C]
+ run_args = [a, w, c]
+
func = tvm.build(
s,
- [A, W, C],
+ compile_args,
target,
name="relu_%d_%d_%d_%d_%d_%d_%d_%d"
% (batch, in_channel, in_size, num_filter, kernel, stride,
padding_sum, dilation),
)
- func(a, w, c)
- tvm.testing.assert_allclose(c.numpy(), c_np, rtol=1e-5)
- verify_workload_padding()
+ if build_only:
+ return
- for target in ["cuda"]:
- check_target(target)
+ print("Running on target: %s" % target)
+ func(*run_args)
[email protected]("in_dtype", ["int8", "uint8"])
-def test_conv2d_nchw(in_dtype):
- with Int8Fallback():
- # ResNet18 workloads where channels in / out are multiple of
oc_block_factor
- verify_conv2d_NCHWc_int8(in_dtype, 1, 64, 56, 64, 3, 1, 1)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 64, 56, 64, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 64, 56, 128, 3, 2, 1)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 64, 56, 128, 1, 2, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 128, 28, 128, 3, 1, 1)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 128, 28, 256, 3, 2, 1)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 128, 28, 256, 1, 2, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 256, 14, 256, 3, 1, 1)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 256, 14, 512, 3, 2, 1)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 256, 14, 512, 1, 2, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 512, 7, 512, 3, 1, 1)
+ tvm.testing.assert_allclose(c.numpy(), c_np, rtol=1e-5)
- # bias, relu
- verify_conv2d_NCHWc_int8(in_dtype, 1, 64, 56, 64, 3, 1, 1,
add_relu=True)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 64, 56, 64, 3, 1, 1,
add_bias=True)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 64, 56, 64, 3, 1, 1,
add_bias=True, add_relu=True)
+ targets = [
+ (
+ "cuda",
+ lambda a, w, s, p, d, l, ol, o: topi.cuda.conv2d_NCHWc_int8(a,
w, s, p, d, l, o),
+ topi.cuda.schedule_conv2d_NCHWc_int8,
+ 4,
+ False,
+ ),
+ # Disable on CI since it does not support spirv int8 dot product
+ # (
+ # "vulkan -from_device=0",
+ # lambda a, w, s, p, d, l, ol, o:
topi.cuda.conv2d_NCHWc_int8(a, w, s, p, d, l, o),
+ # topi.cuda.schedule_conv2d_NCHWc_int8,
+ # 4,
+ # False,
+ # ),
+ ]
- # dilation = 2
- verify_conv2d_NCHWc_int8(in_dtype, 1, 64, 56, 64, 3, 1, 1, dilation=2)
+ build_only_aarch64 = platform.machine() != "aarch64"
- # batch size
- verify_conv2d_NCHWc_int8(in_dtype, 4, 64, 56, 64, 3, 1, 1)
- verify_conv2d_NCHWc_int8(in_dtype, 9, 64, 56, 64, 3, 1, 1)
+ targets.append(
+ (
+ "llvm -device arm_cpu -mtriple aarch64-linux-gnu
-mattr=+neon,+v8.2a,+dotprod",
+ topi.arm_cpu.conv2d_NCHWc_int8,
+ topi.arm_cpu.schedule_conv2d_NCHWc_int8,
+ 8,
+ build_only_aarch64,
+ )
+ )
- # weird workloads
- verify_conv2d_NCHWc_int8(in_dtype, 4, 4, 4, 8, 4, 4, 4)
+ if in_dtype == "int8":
+ targets += [
+ (
+ "llvm -device arm_cpu -mtriple aarch64-linux-gnu
-mattr=+neon",
+ topi.arm_cpu.conv2d_NCHWc_int8,
+ topi.arm_cpu.schedule_conv2d_NCHWc_int8,
+ 8,
+ build_only_aarch64,
+ ),
+ (
+ "rocm -mattr=+dotprod",
+ lambda a, w, s, p, d, l, ol, o:
topi.cuda.conv2d_NCHWc_int8(
+ a, w, s, p, d, l, o
+ ),
+ topi.cuda.schedule_conv2d_NCHWc_int8,
+ 4,
+ False,
+ ),
+ ]
+
+ for target, compute, schedule, oc_block_factor, build_only in targets:
+ check_target(target, compute, schedule, oc_block_factor,
build_only)
+
+
+# Conv2d NCHW int8 schedule testing. Internally, it uses NCHWc schedule. So,
just
+# performing basic testing - one test for all different scenarios - batch,
dilation etc..
[email protected]("in_dtype", ["int8", "uint8"])
[email protected](
+ "params",
+ [
+ (1, 64, 56, 64, 3, 1, 1, 1, False, False),
+ (1, 64, 56, 64, 3, 1, 1, 1, False, True),
+ (1, 64, 56, 64, 3, 1, 1, 2, False, False),
+ (9, 64, 56, 64, 3, 1, 1, 1, False, False),
+ (4, 4, 4, 4, 4, 4, 4, 1, False, False),
+ (1, 32, 149, 32, 3, 1, 0, 1, False, False),
+ (7, 32, 149, 32, 3, 1, 0, 1, False, False),
+ (1, 32, 35, 64, 7, 2, (0, 0, 1, 1), 1, False, False),
+ (1, 32, 35, 64, 7, 2, (0, 0, 2, 2), 1, False, False),
+ ],
+)
+def test_conv2d_nchw_int8(in_dtype, params):
+ with Int8Fallback():
+ (
+ batch,
+ in_channel,
+ in_size,
+ num_filter,
+ kernel,
+ stride,
+ padding,
+ dilation,
+ add_bias,
+ add_relu,
+ ) = params
+ pad_top, pad_left, pad_bottom, pad_right = get_pad_tuple(padding,
(kernel, kernel))
+ padding_sum = pad_top + pad_left + pad_bottom + pad_right
+ print(
+ "Workload: (%d, %d, %d, %d, %d, %d, %d, %d)"
+ % (batch, in_channel, in_size, num_filter, kernel, stride,
padding_sum, dilation)
+ )
- # inception v3 workloads where channels in / out are multiple of
oc_block_factor
- verify_conv2d_NCHWc_int8(in_dtype, 1, 32, 149, 32, 3, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 32, 147, 64, 3, 1, 1)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 64, 73, 80, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 80, 73, 192, 3, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 192, 35, 64, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 192, 35, 48, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 48, 35, 64, 5, 1, 2)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 64, 35, 96, 3, 1, 1)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 96, 35, 96, 3, 1, 1)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 192, 35, 32, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 256, 35, 64, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 256, 35, 48, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 288, 35, 64, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 288, 35, 48, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 288, 35, 384, 3, 2, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 96, 35, 96, 3, 2, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 768, 17, 192, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 768, 17, 128, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 128, 17, 128, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 128, 17, 192, 7, 1, 3)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 128, 17, 128, 7, 1, 3)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 128, 17, 192, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 768, 17, 160, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 160, 17, 160, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 160, 17, 192, 7, 1, 3)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 160, 17, 160, 7, 1, 3)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 160, 17, 192, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 192, 17, 192, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 192, 17, 192, 7, 1, 3)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 192, 17, 320, 3, 2, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 192, 17, 192, 3, 2, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 1280, 8, 320, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 1280, 8, 384, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 384, 8, 384, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 384, 8, 384, 3, 1, 1)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 1280, 8, 448, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 448, 8, 384, 3, 1, 1)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 1280, 8, 192, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 2048, 8, 320, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 2048, 8, 384, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 2048, 8, 448, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 2048, 8, 192, 1, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 1024, 19, 88, 3, 1, 1)
+ in_height = in_width = in_size
- # batch > 1
- verify_conv2d_NCHWc_int8(in_dtype, 7, 32, 149, 32, 3, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 8, 32, 149, 32, 3, 1, 0)
- verify_conv2d_NCHWc_int8(in_dtype, 32, 32, 149, 32, 3, 1, 0)
+ A = te.placeholder((batch, in_channel, in_height, in_width), name="A",
dtype=in_dtype)
+ W = te.placeholder((num_filter, in_channel, kernel, kernel), name="W",
dtype=in_dtype)
+ bias = te.placeholder((num_filter, 1, 1), name="bias", dtype=in_dtype)
- # Asymmetric padding
- verify_conv2d_NCHWc_int8(in_dtype, 1, 32, 35, 64, 7, 2, (0, 0, 1, 1))
- verify_conv2d_NCHWc_int8(in_dtype, 1, 64, 8, 128, 3, 1, (3, 3, 2, 2))
- verify_conv2d_NCHWc_int8(in_dtype, 1, 64, 8, 64, 1, 1, (1, 2, 2, 1))
- verify_conv2d_NCHWc_int8(in_dtype, 1, 64, 17, 192, 1, 1, (1, 2))
- verify_conv2d_NCHWc_int8(in_dtype, 1, 64, 8, 64, 3, 1, (3, 1))
- verify_conv2d_NCHWc_int8(in_dtype, 1, 128, 8, 384, 3, 1, (0, 2))
- verify_conv2d_NCHWc_int8(in_dtype, 1, 64, 8, 64, 1, 1, "VALID")
- verify_conv2d_NCHWc_int8(in_dtype, 1, 392, 8, 64, 3, 1, "VALID")
- verify_conv2d_NCHWc_int8(in_dtype, 1, 512, 19, 64, 1, 1, "SAME")
- verify_conv2d_NCHWc_int8(in_dtype, 1, 64, 16, 32, 2, 1, "SAME")
- verify_conv2d_NCHWc_int8(in_dtype, 1, 64, 8, 64, 3, 1, (1, 2, 2, 1),
add_relu=True)
- verify_conv2d_NCHWc_int8(in_dtype, 1, 64, 8, 64, 5, 2, (1, 3),
add_bias=True)
- verify_conv2d_NCHWc_int8(
- in_dtype, 1, 64, 56, 64, 3, 1, "VALID", add_bias=True,
add_relu=True
- )
- verify_conv2d_NCHWc_int8(
- in_dtype, 1, 64, 56, 64, 24, 1, "SAME", add_bias=True,
add_relu=True
- )
+ a_shape = get_const_tuple(A.shape)
+ w_shape = get_const_tuple(W.shape)
+ bias_shape = get_const_tuple(bias.shape)
+ dtype = A.dtype
+
+ @memoize("topi.tests.test_topi_conv2d_int8.verify_conv2d_nchw")
+ def get_ref_data():
+ a_np = np.random.randint(low=-128, high=127,
size=a_shape).astype(dtype)
+ w_np = np.random.randint(low=-128, high=128,
size=w_shape).astype(dtype)
+ b_np = np.random.uniform(size=bias_shape).astype(dtype)
+ dw_np = tvm.topi.testing.dilate_python(w_np, (1, 1, dilation,
dilation))
+ c_np = tvm.topi.testing.conv2d_nchw_python(a_np, dw_np, stride,
padding).astype(dtype)
- # Conv2d NCHW int8 schedule testing. Internally, it uses NCHWc
schedule. So, just
- # performing basic testing - one test for all different scenarios -
batch, dilation etc..
- verify_conv2d_nchw_int8(in_dtype, 1, 64, 56, 64, 3, 1, 1)
- verify_conv2d_nchw_int8(in_dtype, 1, 64, 56, 64, 3, 1, 1,
add_relu=True)
- verify_conv2d_nchw_int8(in_dtype, 1, 64, 56, 64, 3, 1, 1, dilation=2)
- verify_conv2d_nchw_int8(in_dtype, 9, 64, 56, 64, 3, 1, 1)
- verify_conv2d_nchw_int8(in_dtype, 4, 4, 4, 4, 4, 4, 4)
- verify_conv2d_nchw_int8(in_dtype, 1, 32, 149, 32, 3, 1, 0)
- verify_conv2d_nchw_int8(in_dtype, 7, 32, 149, 32, 3, 1, 0)
- verify_conv2d_nchw_int8(in_dtype, 1, 32, 35, 64, 7, 2, (0, 0, 1, 1))
- verify_conv2d_nchw_int8(in_dtype, 1, 32, 35, 64, 7, 2, (0, 0, 2, 2))
+ if add_bias:
+ b_np = np.random.uniform(size=bias_shape).astype(dtype)
+ c_np += b_np
+ if add_relu:
+ c_np = np.maximum(c_np, 0)
+ return a_np, w_np, b_np, c_np
-def test_conv2d_nhwc():
- with Int8Fallback():
- # Subset of inception v3 expanded (dilation > 1, batch > 1, 'VALID'
padding)
- verify_conv2d_NHWC_gemm_int8(1, 3, 299, 32, 3, 2, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 32, 149, 32, 3, 1, "SAME", dilation=2)
- verify_conv2d_NHWC_gemm_int8(4, 32, 147, 64, 3, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 64, 73, 80, 1, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 80, 73, 192, 3, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 192, 35, 48, 1, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 192, 35, 64, 1, 1, "VALID")
- verify_conv2d_NHWC_gemm_int8(1, 192, 35, 32, 1, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 48, 35, 64, 5, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 96, 35, 96, 3, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 256, 35, 48, 1, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 256, 35, 64, 1, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 288, 35, 64, 1, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 288, 35, 48, 1, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 96, 35, 96, 3, 2, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 128, 17, 192, 7, 1, "SAME", dilation=2)
- verify_conv2d_NHWC_gemm_int8(1, 160, 17, 160, 7, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 160, 17, 192, 1, 1, "VALID")
- verify_conv2d_NHWC_gemm_int8(1, 192, 17, 192, 1, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 768, 5, 128, 1, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 192, 17, 320, 3, 2, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 192, 17, 192, 3, 2, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 1280, 8, 192, 1, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 1280, 8, 384, 1, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 1280, 8, 320, 1, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 1280, 8, 448, 1, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 384, 8, 384, 1, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 384, 8, 384, 3, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 448, 8, 384, 3, 1, "VALID")
- verify_conv2d_NHWC_gemm_int8(1, 2048, 8, 320, 1, 1, "SAME")
- verify_conv2d_NHWC_gemm_int8(1, 2048, 8, 448, 1, 1, "SAME",
add_bias=True, add_relu=True)
- verify_conv2d_NHWC_gemm_int8(1, 2048, 8, 192, 1, 1, "SAME",
add_bias=True)
-
- # Let's also verify that it compiles fine on AArch64 targets
- compile_conv2d_NHWC_gemm_int8_arm(1, 3, 299, 32, 3, 2, "SAME")
+ a_np, w_np, b_np, c_np = get_ref_data()
+
+ def verify_workload_padding():
+ _, _, out_height, out_width = get_const_tuple(c_np.shape)
+ wkl = _get_workload(A, W, (stride, stride), padding, dilation,
dtype)
+
+ # for testing functionality,
+ # we choose arbitrary int32_lanes and num_int8_elements can divide
the channel,
+ # regardless of the performance.
+ int32_lanes, num_int8_elements = num_filter, in_channel
+
+ # check if tile_ow candidates are the factors of the right output
weight.
+ cfg = autotvm.get_config()
+ fallback_schedule_cpu_common_int8(cfg, wkl, int32_lanes,
num_int8_elements)
+ ow_tile = np.prod(cfg["tile_ow"].size)
+
+ tvm.testing.assert_allclose(ow_tile, out_width)
+
+ def check_target(target):
+ dev = tvm.device(target, 0)
+ if not tvm.testing.device_enabled(target):
+ print("Skip because %s is not enabled" % target)
+ return
+ if target == "cuda" and not
tvm.contrib.nvcc.have_int8(dev.compute_version):
+ print("Skip because int8 intrinsics are not available")
+ return
Review Comment:
I added the `pytest.skip`. I experimented with hoisting out the functions
since all the tests in that file do something similar, but annoyingly the
functions are all subtly different and depend on pretty much all the parameters
passed to the test and defined in the test, also compute definitions,
schedules, utility functions etc which would all need to be passed as
arguments, so it didn't look like it was worth it.
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