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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/08/08 17:19:15 UTC
[GitHub] [tvm] mehrdadh commented on a diff in pull request #12294: [Hexagon] Add tests for 2 operators that utilize Qualcomm HVX intrinsics.
mehrdadh commented on code in PR #12294:
URL: https://github.com/apache/tvm/pull/12294#discussion_r940479110
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tests/python/contrib/test_hexagon/test_mmul_hvx_intrinsics.py:
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@@ -0,0 +1,205 @@
+# 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.
+
+import numpy as np
+import tvm
+import tvm.testing
+
+from numpy.random import default_rng
+from tvm.script import tir as T
+from tvm.tir.function import TensorIntrin
+
+from tests.python.contrib.test_hexagon.mmul_unit8_hvx_intrin import get_mm_uint8_intrin
+from tests.python.contrib.test_hexagon.quantization_utils import quantize_array, quantize_uint8
+
+UNROLL_FACTOR = 4 # This must match the hard-coded unrolling in mm_uint8_intrinsic().
+
+
+def can_tensorize(n, m, k):
+ return m % (4 * UNROLL_FACTOR) == k % 32 == 0
+
+
+def blockify_matrix(B):
+ """
+ inputs
+ ------
+ B : numpy 2D array (of M x K ) to be blockified
+
+ outputs
+ -------
+ BB : blockified B as array of dimensions (M/4) x K x 4
+
+ blockification is in preparation for HVX ops on 128B 'vectors'
+ assuming input is of type int8 or uint8, and matrix multiplication uses vrmpy to accumulate to int32
+ - once for each block - then K/32 such vectors will be required to carry out the operation.
+
+ if B is height x width = M x K, then output is array of K blocks in x direction,
+ i.e. as [block1, block2, ... blockK ] where each block has dimensions M/4 x 4.
+ Specifically:
+
+ B = [ B[1,1] B[1,2] ... B[1,K] ]
+ [ B[2,1] B[2,2] ... B[2,K] ]
+ .
+ .
+ .
+ [ B[M,1] B[M,2] ... B[M,K] ]
+
+
+ BB = | | |
+ | | |
+ [ B[1,1] B[2,1] B[3,1] B[4,1] | B[1,2] B[2,2] B[3,2] B[4,2] | ... | B[1,K] B[2,K] B[3,K] B[4,K] ]
+ [ B[5,1] B[6,1] B[7,1] B[8,1] | B[5,2] B[6,2] B[7,2] B[8,2] | ... | B[5,K] B[6,K] B[7,K] B[8,K] ]
+ . | . | . | .
+ . | . | . | .
+ . | . | . | .
+ [ B[M-3,1] B[M-2,1] B[M-1,1] B[M,1] | B[M-3,2] B[M-2,2] B[M-1,2] B[M,2] | ... | B[M-3,K] B[M-2,K] B[M-1,K] B[M,K] ]
+ | | |
+ | | |
+ | | |
+ | | |
+ ^ | ^ | | ^
+ | | | | | |
+ | | | | | |
+ | | | | | |
+ block 1 block 2 | | block K
+
+ """
+ BT, M, K = B.shape
+ assert M % 4 == 0
+ out_height = M // 4
+ out_shape = tuple((BT, out_height, K, 4))
+ BB = np.zeros(out_shape).astype(B.dtype) # block form of B
+ for bt in range(BT):
+ for j in range(0, out_height):
+ for k in range(0, K):
+ for b in range(0, 4):
+ y = b + 4 * j
+ BB[bt, j, k, b] = B[bt, y, k]
+ return BB
+
+
+def setup_test(b, m, n, k):
+ a_shape = (b, n, m)
+ b_shape = (b, m, k)
+
+ rng = default_rng()
+ a = rng.integers(1, 16, a_shape, dtype="uint8")
+ b = rng.integers(1, 16, b_shape, dtype="uint8")
+
+ a_q, a_min, a_max = quantize_array(a.reshape(a.size), a.size)
+ b_q, b_min, b_max = quantize_array(b.reshape(b.size), b.size)
+ a_q = np.array(a_q, dtype="uint8").reshape(a_shape)
+ b_q = np.array(b_q, dtype="uint8").reshape(b_shape)
+ a_offset = quantize_uint8(0.0, a_min, a_max)
+ b_offset = quantize_uint8(0.0, b_min, b_max)
+ if can_tensorize(n, m, k):
+ bb = blockify_matrix(b_q) # blockification is only used by the tensorized version
+ bb = bb.reshape(-1) # go via 1D rep in case there are stride / offset issues
+ bb = bb.reshape(b_shape)
+ else:
+ bb = []
+
+ a_f = np.array(a_q, dtype="int32").reshape(a_shape)
+ b_f = np.array(b_q, dtype="int32").reshape(b_shape)
+ expected_output = np.matmul(a_f, b_f)
+
+ intrin_name = "mm.uint8_{}x{}x{}".format(m, n, k)
+ try:
+ TensorIntrin.register(intrin_name, *get_mm_uint8_intrin(m, n, k))
+ except:
+ print("Intrinsic already registered.")
+
+ return a_q, b_q, bb, a_offset, b_offset, intrin_name, expected_output
+
+
+class TestMatMulVec:
+
+ batches, m, n, k = tvm.testing.parameters(
+ (1, 128, 768, 768),
+ (1, 128, 768, 3072),
+ (1, 128, 3072, 768),
+ (1, 128, 128, 64),
+ (1, 128, 64, 128),
+ )
+
+ @tvm.testing.requires_hexagon
+ def test_matmul_intrinsics(self, hexagon_session, batches, m, n, k):
+
+ out_shape = (batches, n, k)
+
+ a_q, b_q, bb, a_offset, b_offset, intrin_name, out_ref = setup_test(batches, m, n, k)
+
+ @T.prim_func
+ def operator(a: T.handle, b: T.handle, c: T.handle, offsets: T.handle) -> None:
+ T.func_attr({"global_symbol": "main", "tir.noalias": True})
+ A = T.match_buffer(a, [batches, n, m], dtype="uint8")
+ B = T.match_buffer(b, [batches, m, k], dtype="uint8")
+ C = T.match_buffer(c, [batches, n, k], dtype="int32")
+ OFFSETS = T.match_buffer(offsets, [2], dtype="uint8")
+ # body
+ with T.block("root"):
+ for i0, i1, i2, i3 in T.grid(batches, m, n, k):
+ with T.block("C"):
+ batch, y, x, j = T.axis.remap("SSSR", [i0, i1, i2, i3])
+ C[batch, y, x] = C[batch, y, x] + T.cast(
+ A[batch, y, j] - OFFSETS[0], "int32"
+ ) * T.cast(B[batch, j, x] - OFFSETS[1], "int32")
+
+ ir_module = operator
+ sch = tvm.tir.Schedule(ir_module, debug_mask="all")
+
+ block = sch.get_block("C")
+ _, y, _, _ = sch.get_loops(block)
+ sch.tensorize(y, intrin_name)
+
+ A = tvm.tir.decl_buffer(a_q.shape, name="A", dtype="uint8")
+ B = tvm.tir.decl_buffer(b_q.shape, name="B", dtype="uint8")
+ C = tvm.tir.decl_buffer(out_shape, name="C", dtype="int32")
+ OFFSETS = tvm.tir.decl_buffer((2), name="OFFSETS", dtype="uint8")
+
+ target_hexagon = tvm.target.hexagon("v68", link_params=True)
+ func_tir = tvm.build(
+ sch.mod,
+ [A, B, C, OFFSETS],
+ tvm.target.Target(target_hexagon, host=target_hexagon),
+ name="qmmul_vrmpy",
+ )
+ module = hexagon_session.load_module(func_tir)
+
+ c = np.zeros(out_shape, dtype="int32")
+ offsets = np.array([a_offset, b_offset], dtype="uint8")
+
+ a_hexagon = tvm.runtime.ndarray.array(a_q, device=hexagon_session.device)
+ b_hexagon = tvm.runtime.ndarray.array(bb, device=hexagon_session.device)
+ c_hexagon = tvm.runtime.ndarray.array(c, device=hexagon_session.device)
+ offsets_hexagon = tvm.runtime.ndarray.array(offsets, device=hexagon_session.device)
+
+ module(a_hexagon, b_hexagon, c_hexagon, offsets_hexagon)
+ evaluator = module.time_evaluator(module.entry_name, hexagon_session.device, number=1)
+ time_ms = evaluator(a_hexagon, b_hexagon, c_hexagon, offsets_hexagon).mean * 1e3
+ print("Input Shape: {}. Conv time elapsed: {} ms".format((batches, m, n, k), time_ms))
+
+ out = c_hexagon.numpy()
+ out_a = out.reshape(batches * n * k)
+ out_req, _, _ = quantize_array(out_a, batches * n * k)
+ out_req = np.array(out_req).reshape(batches, n, k)
+
+ out_ref_a = out_ref.reshape(batches * n * k)
+ out_ref_q, _, _ = quantize_array(out_ref_a, batches * n * k)
+ out_ref_q = np.array(out_ref_q).reshape(batches, n, k)
+
+ tvm.testing.assert_allclose(out_req, out_ref_q, atol=2.0, rtol=0.0)
Review Comment:
`tvm.testing.main()` is missing
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