adstraw commented on code in PR #13381: URL: https://github.com/apache/tvm/pull/13381#discussion_r1023110350
########## tests/python/contrib/test_hexagon/test_cache_read_write.py: ########## @@ -1,226 +0,0 @@ -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. - -""" Lower cache_read and cache_write to Hexagon DMA via tensorize """ - -import numpy as np - -import tvm.testing -from tvm import te, tir -from tvm.contrib.hexagon.session import Session -from tvm.script import tir as T - -from .infrastructure import get_hexagon_target - - -def intrin_mem_copy(shape, dtype, dst_scope, src_scope): - """Define and return tensor intrinsic for mem copy""" - src = te.placeholder(shape=shape, dtype=dtype, name="src") - dst = te.compute(shape, lambda i: src[i], name="dst") - size = shape[0] * np.dtype(dtype).itemsize - - src_buffer = tvm.tir.decl_buffer( - shape, - dtype, - scope=src_scope, - offset_factor=1, - name="mem_copy_src_buffer", - ) - - dst_buffer = tvm.tir.decl_buffer( - shape, - dtype, - scope=dst_scope, - offset_factor=1, - name="mem_copy_dst_buffer", - ) - - zero_indices = [0 for _ in shape] - - def intrin_func(ins, outs): - ir_builder = tvm.tir.ir_builder.create() - - _src = ins[0] - _dst = outs[0] - - dst_handle = ir_builder.buffer_ptr(dst_buffer) - src_handle = ir_builder.buffer_ptr(src_buffer) - - ir_builder.emit( - tvm.tir.call_intrin( - "handle", - "tir.mem_copy", - tvm.tir.call_intrin("handle", "tir.address_of", dst_handle[zero_indices]), - tvm.tir.call_intrin("handle", "tir.address_of", src_handle[zero_indices]), - size, - ) - ) - return ir_builder.get() - - return te.decl_tensor_intrin(dst.op, intrin_func, binds={src: src_buffer, dst: dst_buffer}) - - -def verify(hexagon_session: Session, schedule, x_tensor, y_tensor, z_tensor, size): - """Verify correctness with reference from numpy""" - print(tvm.lower(schedule, [x_tensor, y_tensor, z_tensor])) - - func = tvm.build( - schedule, - [x_tensor, y_tensor, z_tensor], - get_hexagon_target("v68"), - name="dmacpy", - ) - - mod = hexagon_session.load_module(func) - x_array = tvm.nd.array( - np.random.randint(low=-128, high=127, size=size, dtype=x_tensor.dtype), - device=hexagon_session.device, - ) - y_array = tvm.nd.array( - np.random.randint(low=-128, high=127, size=size, dtype=y_tensor.dtype), - device=hexagon_session.device, - ) - z_array = tvm.nd.array( - np.random.randint(low=-128, high=127, size=size, dtype=z_tensor.dtype), - device=hexagon_session.device, - ) - mod["dmacpy"](x_array, y_array, z_array) - - ref = x_array.numpy() + y_array.numpy() - np.testing.assert_equal(z_array.numpy(), ref) - - [email protected]_hexagon -def test_cache_read_write(hexagon_session: Session): - """Test cache_read and cache_write to global.vtcm for hexagon""" - size = 128 - outer_shape = (size,) - factor = 16 - inner_shape = (factor,) - dtype = "int8" - - x_tensor = te.placeholder(shape=outer_shape, dtype=dtype, name="x") - y_tensor = te.placeholder(shape=outer_shape, dtype=dtype, name="y") - z_tensor = te.compute(outer_shape, lambda i: x_tensor[i] + y_tensor[i], name="z") - s = te.create_schedule(z_tensor.op) - - x_vtcm = s.cache_read(x_tensor, "global.vtcm", [z_tensor]) - y_vtcm = s.cache_read(y_tensor, "global.vtcm", [z_tensor]) - z_vtcm = s.cache_write(z_tensor, "global.vtcm") - - zouter, _ = s[z_vtcm].split(z_vtcm.op.axis[0], factor=factor) - - s[x_vtcm].compute_at(s[z_vtcm], zouter) - s[y_vtcm].compute_at(s[z_vtcm], zouter) - - mem_copy_read = intrin_mem_copy(inner_shape, dtype, "global.vtcm", "global") - - (cache_read_x,) = s[x_vtcm].op.axis - s[x_vtcm].tensorize(cache_read_x, mem_copy_read) - - (cache_read_y,) = s[y_vtcm].op.axis - s[y_vtcm].tensorize(cache_read_y, mem_copy_read) - - mem_copy_write = intrin_mem_copy(outer_shape, dtype, "global", "global.vtcm") - - (cache_write_z,) = s[z_tensor].op.axis - s[z_tensor].tensorize(cache_write_z, mem_copy_write) - - verify(hexagon_session, s, x_tensor, y_tensor, z_tensor, size) - - -def layout_transform_2d(n): - return [n // 16, te.AXIS_SEPARATOR, n % 16] - - [email protected]_hexagon -def test_cache_read_write_2d(hexagon_session: Session): - """Test 2D cache_read and cache_write to global.vtcm for hexagon""" - size = 128 - outer_shape = (size,) - factor = 16 - inner_shape = (factor,) - dtype = "int8" - - x_tensor = te.placeholder(shape=outer_shape, dtype=dtype, name="x") - y_tensor = te.placeholder(shape=outer_shape, dtype=dtype, name="y") - z_tensor = te.compute(outer_shape, lambda i: x_tensor[i] + y_tensor[i], name="z") - s = te.create_schedule(z_tensor.op) - - x_vtcm = s.cache_read(x_tensor, "global.vtcm", [z_tensor]) - y_vtcm = s.cache_read(y_tensor, "global.vtcm", [z_tensor]) - z_vtcm = s.cache_write(z_tensor, "global.vtcm") - - layout_x_vtcm = s[x_vtcm].transform_layout(layout_transform_2d) - layout_y_vtcm = s[y_vtcm].transform_layout(layout_transform_2d) - _ = s[z_vtcm].transform_layout(layout_transform_2d) - - mem_copy_read = intrin_mem_copy(inner_shape, dtype, "global.vtcm", "global") - s[x_vtcm].tensorize(layout_x_vtcm[1], mem_copy_read) - s[y_vtcm].tensorize(layout_y_vtcm[1], mem_copy_read) - - # The loop schedule over `z` is not modified when calling `transform_layout` - # on `z_vtcm` above therefore we must call `split` to modify the loop schedule - # over `z` to match the layout of `z_vtcm` such that we can accurately write - # `z_vtcm` back to `z` using memory copy intrinsic - _, zinner = s[z_tensor].split(z_tensor.op.axis[0], factor=factor) - mem_copy_write = intrin_mem_copy(inner_shape, dtype, "global", "global.vtcm") - s[z_tensor].tensorize(zinner, mem_copy_write) - - verify(hexagon_session, s, x_tensor, y_tensor, z_tensor, size) - - [email protected]_func -def scale_by_two(buffer_a: T.Buffer[(8192,), "int8"], buffer_c: T.Buffer[(8192,), "int8"]): - for i in T.serial( - 0, - 8192, - ): - with T.block("C"): - buffer_c[i] = buffer_a[i] * T.int8(2) - - -def test_vtcm_lowering(): - """Test lowering with vtcm mem scope""" - mod = tvm.IRModule.from_expr(scale_by_two.with_attr("global_symbol", "main")) - sch = tir.Schedule(mod, debug_mask="all") - block_c = sch.get_block("C") - (flat,) = sch.get_loops(block_c) - outer, _, _, _ = sch.split(flat, factors=[8, 4, 2, 128]) - cache_block = sch.cache_read(block_c, 0, storage_scope="global.vtcm") - sch.compute_at(cache_block, outer) - lowered = tvm.lower(sch.mod["main"]) - - def ir_module_has_allocate_nodes(irmod): - nallocs = 0 - - def _visit(stmt): - nonlocal nallocs - if isinstance(stmt, tvm.tir.Allocate): - nallocs += 1 - - tvm.tir.stmt_functor.post_order_visit(irmod["main"].body, _visit) - return nallocs - - assert not ir_module_has_allocate_nodes(lowered), ( - "AllocateNode found in lowered IRModule, " - "VTCM allocations should have been lowered to tir.nd_mem_alloc_with_scope" - ) Review Comment: Good point. I will restore `test_vtcm_lowering` and rename the file to `test_vtcm.py` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
