guberti commented on code in PR #13242:
URL: https://github.com/apache/tvm/pull/13242#discussion_r1030642924
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python/tvm/topi/arm_cpu/mprofile/dsp/micro_kernel/tensordot.py:
##########
@@ -14,142 +14,333 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
-"""Computes a "jumpy tensordot" operator, which can be used to tensorize many
common operators
-including regular conv2d, depthwise conv2d, and grouped conv2d provided the
data and kernel layouts
-are the optimal ones. When groups=1, the optimal data layout is NHWC and
kernel layout is OHWI. When
-this is a depthwise convolution, the optimal data layout is NCHW and kernel
layout is OIHW."""
+"""Generates optimized code to compute a tensor dot product on ARMv7E-M.
+This function can be used to tensorize many common operators including regular
conv2d, depthwise
+conv2d, and grouped conv2d for some data and kernel layouts. When for regular
convolution, use data
+layout HHWC and kernel layout OHWI. For depthwise convolution, use data layout
data layout is NCHW
+and kernel layout OIHW.
+"""
+
+from itertools import chain
import textwrap
+from typing import Iterator, Tuple
-from tvm import te, tir
-from .common import num_simd_lanes_per_word
+def _get_c_function_name(split_size, dimensions, offsets, x_strides):
+ """Generates a C function name for tensordot.
+ We do not need a suffix, as the generated function will have an #include
guard. Unlike other
+ microTVM operators, _get_c_function_name is never called externally.
+ """
+ tensor_w, kernel_h, kernel_w = dimensions
+ return (
+ f"tensordot_opt_x{split_size}_int16_w{tensor_w}_"
+ + f"{kernel_h}x{kernel_w}_"
+ + "".join(map(str, offsets))
+ + (f"_{x_strides[0]}_{x_strides[1]}" if split_size > 1 else "")
+ )
-def _get_func_name(in_dtype, tensor_h, jump, tensor_w, suffix):
- """Gets the C function name of the tensordot function."""
- return f"tensordot_{in_dtype}_h{tensor_h}_j{jump}_w{tensor_w}_{suffix}"
+def _init_biased_accumulators(split_size):
+ """Generates code to load the bias into the accumulators.
-def make_intrin_tensordot(slices, strides, tensordot_params):
- """Helper function for constructing tensordot intrinsic. We can't
construct the whole thing here
- (as multiple schedules use tensordot and each must build the intrinstic
differently) but we can
- build part here to simplify the code."""
+ Addition is commutative, so we could add the bias before, during, or after
performing our
+ multiply-accumulate operations. Where we add the bias does not change the
overflow behavior.
- # in_dtype, tensor_h, jump, tensor_w, suffix = tensordot_params
- data, kernel, output = slices
- data_strides, kernel_strides = strides
+ Doing the bias add takes one cycle either way (if done at the beginning we
can't use a SMULXY
+ trick to set sum_i to zero for "free"). However, doing it at the beginning
frees up a register,
+ so we'll do it first.
+ """
+ assignments = map(lambda x: f"sum_{x:x} = *bias", range(split_size))
+ joined_assignments = ", ".join(assignments)
+ return f"int {joined_assignments};"
- data_buf = tir.decl_buffer(
- data.shape, data.dtype, name="data", offset_factor=1,
strides=data_strides
- )
- kernel_buf = tir.decl_buffer(
- kernel.shape,
- kernel.dtype,
- name="kernel",
- offset_factor=1,
- strides=kernel_strides,
- )
- output_buf = tir.decl_buffer(
- output.shape, output.dtype, name="output", offset_factor=1, strides=[1]
- )
- def intrin_func(ins, outs):
- builder = tir.ir_builder.create()
- builder.emit(
- tir.call_extern(
- "int32",
- _get_func_name(*tensordot_params),
- outs[0].access_ptr("w"),
- ins[0].access_ptr("r"),
- ins[1].access_ptr("r"),
- )
- )
- return builder.get()
+def _get_tensor_halfwords(dimensions, offset, split_size, in_stride) ->
Iterator:
+ tensor_w, kernel_h, kernel_w = dimensions
- return te.decl_tensor_intrin(
- output.op,
- intrin_func,
- binds={data: data_buf, kernel: kernel_buf, output: output_buf},
- )
+ split_max = (split_size - 1) * in_stride
+ for y in range(kernel_h):
+ if y * tensor_w % 2 + offset == 1:
+ yield None
+ for x in range(kernel_w + split_max):
+ yield (y, x)
+ if (y * tensor_w + kernel_w + split_max + offset) % 2 == 1:
+ yield None
+
+
+def _get_kernel_halfwords(dimensions, offset) -> Iterator:
+ _, kernel_h, kernel_w = dimensions
+ if offset == 1:
+ yield None
+ for y in range(kernel_h):
+ for x in range(kernel_w):
+ yield (y, x)
+ if (kernel_h * kernel_w + offset) % 2 == 1:
+ yield None
+
+
+def _get_int16_alias(position) -> str:
+ if not position:
+ return "unknown"
+ y, x = position
+ return f"y{y:0>2x}_x{x:0>2x}"
+
+
+def _load_tensor_vars(halfwords, tensor_w) -> Iterator[str]:
+ assert len(halfwords) % 2 == 0
+ offset = int(not bool(halfwords[0]))
+
+ for i in range(0, len(halfwords), 2):
+ var_name = "__".join(map(_get_int16_alias, halfwords[i : i + 2]))
+ y, x = halfwords[i + 1] or halfwords[i]
+ tensor_index = (y * tensor_w + x + offset) // 2
+ yield f"int tensor__{var_name} = tensor[{tensor_index}];"
+
+
+def _load_kernel_vars(halfwords) -> Iterator[str]:
+ assert len(halfwords) % 2 == 0
+ for i in range(0, len(halfwords), 2):
+ var_name = "__".join(map(_get_int16_alias, halfwords[i : i + 2]))
+ yield f"int kernel__{var_name} = kernel[{i // 2}];"
-def tensordot_impl(in_dtype: str, tensor_h: int, jump: int, tensor_w: int,
suffix: str) -> str:
- """Generates C code for taking the dot products of two `tensor_h` *
`tensor_w` tensors. Also has
- a `jump` argument that advances the pointer of one tensor by that many
words after each row. The
- `jump` and `tensor_w` values must be word-aligned for the input data type,
as non-word-aligned
- memory access is slow on the Cortex-M series. Depending on the input
datatype, the code may
- contain DSP instructions for Arm v7e-m. C code contains DSP instructions
for Arm v7e-m. See
- the below pseudocode for reference:
-
- tensordot(out_ptr, dat_ptr, ker_ptr) {
- sum = 0;
- for (i = 0; i < tensor_h; i++) {
- for (j = 0; j < tensor_w; j++) {
- sum += (*dat_ptr++) * (*ker_ptr++);
- }
- dat_ptr += jump;
- }
- *out_ptr = sum;
- }
+def _get_draft_macs(kernel_dims, tensor_halfwords, kernel_halfwords, offset)
-> Iterator[Tuple]:
+ """Generates an un-optimized list of multiply-accumulate instructions.
+
+ We will optimize these into SIMD instructions later. The tuples in the
returned iterator are
+ organized as:
+
+ (instruction, (arg1_y, arg1_x), (arg2_y, arg2_x))
+
+ We return an iterator so that optimizations may be done by iterator
chaining.
+ """
+
+ def get_var(y, x, halfwords):
+ i = halfwords.index((y, x))
+ if i % 2 == 0:
+ return f"{_get_int16_alias((y, x))}__{_get_int16_alias(halfwords[i
+ 1])}", "b"
+ return f"{_get_int16_alias(halfwords[i - 1])}__{_get_int16_alias((y,
x))}", "t"
+
+ kernel_h, kernel_w = kernel_dims
+ for y in range(kernel_h):
+ for x in range(kernel_w):
+ tensor_var, tensor_half = get_var(y, x + offset, tensor_halfwords)
+ kernel_var, kernel_half = get_var(y, x, kernel_halfwords)
+ instruction = f"smla{tensor_half}{kernel_half}"
+ yield instruction, f"tensor__{tensor_var}", f"kernel__{kernel_var}"
+
+
+def _apply_simd_optimizations(instruction_tuples) -> Iterator[Tuple]:
+ """When possible, fuses single MACs into SIMD MAC instructions.
+
+ The compiler cannot do this automatically, as calling __builtin_arm_smlaxy
forces the SMLAxy
Review Comment:
Yes, the inner reduction loops will always be unrolled (this occurs in
`_get_draft_macs`). We will often unroll even more than this, either as another
unrolled copy of the inner loops for odd-numbered channels (this happens e.g.
for 3x3 depthwise convolutions) or by computing multiple sums at the same times
(i.e. when `num_sums > 1`).
Compared with the naive approach, this does increase code size. However, the
increase is very small - for example, unrolling a 3x3 depthwise convolution
might take ~10 extra instructions, or 0.01 KB more flash size. This is well
worth it, as unrolling dramatically reduces overhead and increases speed by
~2x. The previous `tensordot` implementation also unrolled these loops for the
same reason.
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