| # Owner(s): ["module: fx"] |
| |
| import os |
| import sys |
| |
| import torch |
| from torch.fx import symbolic_trace, subgraph_rewriter |
| from torch.fx.annotate import annotate |
| # Make the helper files in test/ importable |
| from torch.fx.experimental.rewriter import RewritingTracer |
| |
| pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) |
| sys.path.append(pytorch_test_dir) |
| from torch.testing._internal.jit_utils import JitTestCase |
| |
| if __name__ == '__main__': |
| raise RuntimeError("This test file is not meant to be run directly, use:\n\n" |
| "\tpython test/test_fx.py TESTNAME\n\n" |
| "instead.") |
| |
| @torch.fx.wrap |
| def wrapped_gemm_bias_mul(a, b, bias): |
| lin_res = torch.nn.functional.linear(a, b, bias=bias) |
| mul_res = lin_res * a |
| return lin_res, mul_res |
| |
| @torch.fx.wrap |
| def wrapped_gemm_bias_mul_with_c(a, b, bias, c): |
| lin_res = torch.nn.functional.linear(a, b, bias=bias) |
| mul_res = lin_res * c |
| return lin_res, mul_res |
| |
| class TestSubgraphRewriter(JitTestCase): |
| |
| def test_subgraph_rewriter_preserves_logic(self): |
| class M(torch.nn.Module): |
| def forward(self, x): |
| val = torch.neg(x) + torch.relu(x) |
| return torch.add(val, val) |
| |
| def pattern(x): |
| return torch.neg(x) + torch.relu(x) |
| |
| def comparison(x): |
| val = torch.neg(x) + torch.relu(x) |
| return torch.add(val, val) |
| |
| traced = symbolic_trace(M()) |
| comparison_fn = symbolic_trace(comparison) |
| |
| x = torch.rand(1, 3) |
| |
| # Replace `pattern` with the same pattern (shouldn't change |
| # the underlying logic) |
| subgraph_rewriter.replace_pattern(traced, pattern, pattern) |
| |
| traced.graph.lint() |
| |
| ref_output = comparison_fn(x) |
| test_output = traced.forward(x) |
| self.assertEqual(ref_output, test_output) |
| |
| def test_subgraph_rewriter_with_oneliner_pattern(self): |
| class M(torch.nn.Module): |
| def forward(self, x): |
| val = torch.neg(x) |
| return torch.add(val, val) |
| |
| def pattern(x): |
| return torch.neg(x) |
| |
| def replacement(x): |
| return torch.relu(x) |
| |
| def comparison(x): |
| val = torch.relu(x) |
| return torch.add(val, val) |
| |
| traced = symbolic_trace(M()) |
| comparison_fn = symbolic_trace(comparison) |
| |
| x = torch.rand(1, 3) |
| |
| subgraph_rewriter.replace_pattern(traced, pattern, replacement) |
| |
| traced.graph.lint() |
| |
| ref_output = comparison_fn(x) |
| test_output = traced.forward(x) |
| self.assertEqual(ref_output, test_output) |
| |
| def test_subgraph_rewriter_single_pattern_match(self): |
| class M(torch.nn.Module): |
| def forward(self, x): |
| val = torch.neg(x) + torch.relu(x) |
| return torch.add(val, val) |
| |
| def pattern(x): |
| return torch.neg(x) + torch.relu(x) |
| |
| def replacement(x): |
| return torch.relu(x) |
| |
| def comparison(x): |
| val = torch.relu(x) |
| return torch.add(val, val) |
| |
| traced = symbolic_trace(M()) |
| comparison_fn = symbolic_trace(comparison) |
| |
| x = torch.rand(1, 3) |
| |
| subgraph_rewriter.replace_pattern(traced, pattern, replacement) |
| |
| traced.graph.lint() |
| |
| ref_output = comparison_fn(x) |
| test_output = traced.forward(x) |
| self.assertEqual(ref_output, test_output) |
| |
| def test_subgraph_rewriter_multiple_pattern_match(self): |
| class M(torch.nn.Module): |
| def forward(self, x, w1, w2): |
| m1 = torch.cat([w1, w2]).sum() |
| m2 = torch.cat([w1, w2]).sum() |
| return x + torch.max(m1) + torch.max(m2) |
| |
| def pattern(w1, w2): |
| return torch.cat([w1, w2]).sum() |
| |
| def replacement(w1, w2): |
| return torch.stack([w1, w2]) |
| |
| def comparison(x, w1, w2): |
| m1 = torch.stack([w1, w2]) |
| m2 = torch.stack([w1, w2]) |
| return x + torch.max(m1) + torch.max(m2) |
| |
| traced = symbolic_trace(M()) |
| comparison_fn = symbolic_trace(comparison) |
| |
| x = torch.rand(1, 3) |
| w1 = torch.rand(1, 3) |
| w2 = torch.rand(1, 3) |
| |
| subgraph_rewriter.replace_pattern(traced, pattern, replacement) |
| |
| traced.graph.lint() |
| |
| ref_outs = comparison_fn(x, w1, w2) |
| test_outs = traced.forward(x, w1, w2) |
| self.assertEqual(ref_outs, test_outs) |
| |
| def test_subgraph_rewriter_graph_argument_order(self): |
| class M(torch.nn.Module): |
| def forward(self, x, y): |
| return torch.mm(x, y) |
| |
| def pattern(x, y): |
| return torch.mm(x, y) |
| |
| def comparison(x, y): |
| return torch.mm(x, y) |
| |
| traced = symbolic_trace(M()) |
| comparison_fn = symbolic_trace(comparison) |
| |
| x = torch.randn(3, 4) |
| y = torch.randn(4, 5) |
| |
| subgraph_rewriter.replace_pattern(traced, pattern, pattern) |
| |
| traced.graph.lint() |
| |
| ref_outs = comparison_fn(x, y) |
| test_outs = traced.forward(x, y) |
| self.assertEqual(ref_outs, test_outs) |
| |
| def test_subgraph_rewriter_correct_output_replacement(self): |
| class M(torch.nn.Module): |
| def forward(self, x, y): |
| val = torch.neg(y) + torch.relu(x) |
| return torch.add(val, val) |
| |
| def pattern(x): |
| return torch.relu(x) |
| |
| def replacement(x): |
| return torch.neg(x) |
| |
| def comparison(x, y): |
| val = torch.neg(y) + torch.neg(x) |
| return torch.add(val, val) |
| |
| traced = symbolic_trace(M()) |
| comparison_fn = symbolic_trace(comparison) |
| |
| x = torch.randn(4, 4) |
| y = torch.randn(4, 4) |
| |
| subgraph_rewriter.replace_pattern(traced, pattern, replacement) |
| |
| traced.graph.lint() |
| |
| ref_outs = comparison_fn(x, y) |
| test_outs = traced.forward(x, y) |
| self.assertEqual(ref_outs, test_outs) |
| |
| def test_subgraph_rewriter_traced_as_callable(self): |
| class M(torch.nn.Module): |
| def forward(self, x): |
| val = torch.neg(x) + torch.relu(x) |
| return torch.add(val, val) |
| |
| class Pattern(torch.nn.Module): |
| def forward(self, x): |
| return torch.neg(x) + torch.relu(x) |
| |
| class Replacement(torch.nn.Module): |
| def forward(self, x): |
| return torch.sigmoid(x) |
| |
| def comparison(x): |
| val = torch.sigmoid(x) |
| return torch.add(val, val) |
| |
| traced = symbolic_trace(M()) |
| traced_pattern = symbolic_trace(Pattern()) |
| traced_replacement = symbolic_trace(Replacement()) |
| comparison_fn = symbolic_trace(comparison) |
| |
| x = torch.randn(3, 4) |
| |
| subgraph_rewriter.replace_pattern(traced, traced_pattern, traced_replacement) |
| |
| traced.graph.lint() |
| |
| ref_outs = comparison_fn(x) |
| test_outs = traced.forward(x) |
| self.assertEqual(ref_outs, test_outs) |
| |
| def test_subgraph_rewriter_pattern_is_entire_graph(self): |
| class M(torch.nn.Module): |
| def forward(self, x): |
| a = torch.neg(x) |
| return torch.add(a, a) |
| |
| def pattern(x): |
| a = torch.neg(x) |
| return torch.add(a, a) |
| |
| def replacement(x): |
| a = torch.sigmoid(x) |
| return torch.cat([a, a]) |
| |
| traced = symbolic_trace(M()) |
| comparison_fn = symbolic_trace(replacement) |
| |
| x = torch.randn(3, 4) |
| |
| subgraph_rewriter.replace_pattern(traced, pattern, replacement) |
| |
| traced.graph.lint() |
| |
| ref_outs = comparison_fn(x) |
| test_outs = traced.forward(x) |
| self.assertEqual(ref_outs, test_outs) |
| |
| def test_subgraph_rewriter_pattern_output_pattern_node_can_have_users_that_are_not_matched(self): |
| class M(torch.nn.Module): |
| def forward(self, x): |
| y = torch.relu(x) |
| return torch.neg(y) - y |
| |
| def pattern(x): |
| return torch.relu(x) |
| |
| def replacement(x): |
| return torch.sigmoid(x) |
| |
| def comparison(x): |
| y = torch.sigmoid(x) |
| return torch.neg(y) - y |
| |
| traced = symbolic_trace(M()) |
| comparison_fn = symbolic_trace(comparison) |
| |
| x = torch.randn(3, 4) |
| |
| subgraph_rewriter.replace_pattern(traced, pattern, replacement) |
| |
| traced.graph.lint() |
| |
| ref_outs = comparison_fn(x) |
| test_outs = traced.forward(x) |
| self.assertEqual(ref_outs, test_outs) |
| |
| def test_subgraph_rewriter_internal_pattern_nodes_cannot_have_users_that_are_not_matched(self): |
| class M(torch.nn.Module): |
| def forward(self, x, w1, w2, b1, b2): |
| m0 = torch.cat([w1, w2]) |
| m1 = torch.cat([w1, w2]) |
| m2 = torch.cat([x, b2]) |
| t0 = torch.addmm(b1, m1, m2.t()) |
| t1 = torch.sum(w1, 1) |
| t2 = torch.addmm(b1, m1, m2.t()) |
| return torch.sum(t1), torch.sum(t2) |
| |
| def pattern(x, w1, w2, b1, b2): |
| m1 = torch.cat([w1, w2]) |
| m2 = torch.cat([x, b2]) |
| return torch.addmm(b1, m1, m2.t()) |
| |
| def replacement(x, w1, w2, b1, b2): |
| return torch.cat([x, w1, w2]) |
| |
| traced = symbolic_trace(M()) |
| |
| # Result should be [] since no matches can be found |
| res = subgraph_rewriter.replace_pattern(traced, pattern, replacement) |
| |
| traced.graph.lint() |
| |
| self.assertEqual(res, []) |
| |
| def test_subgraph_rewriter_placeholder_matching(self): |
| """ |
| This tests that a placeholder Node can be matched to a Node with |
| a different number of input Nodes. In the example below, the |
| original traced Module looks like this: |
| |
| opcode target args kwargs |
| ------------- ---------------------------------------------------------- ------------------------ -------- |
| placeholder x () {} |
| call_function <built-in function add> (x, 3) {} |
| call_method dequantize (add,) {} |
| call_function <built-in method sigmoid of type object at 0x7f7c1f440fe0> (dequantize,) {} |
| call_method to (sigmoid, torch.float16) {} |
| output output (to,) {} |
| |
| while the pattern we want to match looks like this: |
| |
| opcode target args kwargs |
| ------------- ---------------------------------------------------------- ------------------------ -------- |
| placeholder x () {} |
| call_method dequantize (x,) {} |
| call_function <built-in method sigmoid of type object at 0x7f7c1f440fe0> (dequantize,) {} |
| call_method to (sigmoid, torch.float16) {} |
| output output (to,) {} |
| |
| Here, we want to be able to match the original graph's |
| `call_function.add` Node with the pattern graph's |
| `plaeholder.x` Node. |
| |
| Credit to Jerry Zhang (GitHub: jerryzh168) for this test case |
| """ |
| class M(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.dtype = torch.float16 |
| |
| def forward(self, x): |
| x += 3 |
| x = x.dequantize() |
| x = torch.sigmoid(x) |
| dtype = self.dtype |
| x = x.to(dtype) |
| return x |
| |
| def pattern(x): |
| x = x.dequantize() |
| x = torch.sigmoid(x) |
| x = x.to(torch.float16) |
| return x |
| |
| def replacement(x): |
| return x |
| |
| def comparison(x): |
| return x + 3 |
| |
| traced = symbolic_trace(M()) |
| comparison_fn = symbolic_trace(comparison) |
| |
| x = torch.randn(3, 4) |
| |
| subgraph_rewriter.replace_pattern(traced, pattern, replacement) |
| |
| traced.graph.lint() |
| |
| ref_outs = comparison_fn(x) |
| test_outs = traced.forward(x) |
| self.assertEqual(ref_outs, test_outs) |
| |
| def test_subgraph_rewriter_replaces_referenced_submodules(self): |
| class M(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.sigmoid = torch.nn.Sigmoid() |
| self.submod = torch.nn.ReLU() |
| |
| def forward(self, x): |
| x = x + 1 |
| return self.submod(self.sigmoid(x)) |
| |
| class Pattern(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.sigmoid = torch.nn.Sigmoid() |
| self.submod = torch.nn.ReLU() |
| |
| def forward(self, x): |
| return self.submod(self.sigmoid(x)) |
| |
| class Replacement(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.tanh = torch.nn.Tanh() |
| self.submod = torch.nn.ReLU() |
| |
| def forward(self, x): |
| return self.submod(self.tanh(x)) |
| |
| class Comparison(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.tanh = torch.nn.Tanh() |
| self.submod = torch.nn.ReLU() |
| |
| def forward(self, x): |
| x = x + 1 |
| return self.submod(self.tanh(x)) |
| |
| traced = symbolic_trace(M()) |
| comparison = Comparison() |
| |
| x = torch.randn(3, 4) |
| |
| subgraph_rewriter.replace_pattern(traced, Pattern(), Replacement()) |
| |
| traced.graph.lint() |
| |
| ref_outs = comparison(x) |
| test_outs = traced.forward(x) |
| self.assertEqual(ref_outs, test_outs) |
| |
| traced.get_submodule("tanh") |
| with self.assertRaisesRegex(AttributeError, "has no attribute"): |
| traced.get_submodule("sigmoid") |
| |
| submod = traced.get_submodule("submod") |
| self.assertEqual(type(submod), torch.nn.ReLU) |
| |
| def test_subgraph_rewriter_annotations_int(self): |
| |
| class M1(torch.nn.Module): |
| def forward(self, x): |
| y: int = x |
| return torch.add(x, y) |
| |
| class M2(torch.nn.Module): |
| def forward(self, x): |
| y = annotate(x, int) |
| return torch.add(x, y) |
| |
| ast_rewriter = RewritingTracer() |
| graph = ast_rewriter.trace(M1()) |
| |
| module = M2() |
| symbolic_traced: torch.fx.GraphModule = symbolic_trace(module) |
| for n, m in zip(symbolic_traced.graph.nodes, graph.nodes): |
| if n.op == 'placeholder': |
| assert n.type == int |
| assert m.type == int |
| |
| def test_subgraph_rewriter_replace_consecutive_submodules(self): |
| |
| def f(x): |
| x = torch.sigmoid(x) |
| x = torch.sigmoid(x) |
| return torch.sigmoid(x) |
| |
| def pattern(x): |
| return torch.sigmoid(x) |
| |
| def replacement(x): |
| return torch.exp(x) |
| |
| def comparison(x): |
| x = torch.exp(x) |
| x = torch.exp(x) |
| return torch.exp(x) |
| |
| traced = symbolic_trace(f) |
| comparison_fn = symbolic_trace(comparison) |
| |
| x = torch.randn(3, 4) |
| |
| subgraph_rewriter.replace_pattern(traced, pattern, replacement) |
| |
| traced.graph.lint() |
| |
| ref_outs = comparison_fn(x) |
| test_outs = traced.forward(x) |
| self.assertEqual(ref_outs, test_outs) |
| |
| def test_subgraph_rewriter_with_overlapping_matches(self): |
| |
| def f(x): |
| x = torch.sigmoid(x) |
| x = torch.sigmoid(x) |
| x = torch.sigmoid(x) |
| return torch.sigmoid(x) |
| |
| def pattern(x): |
| x = torch.sigmoid(x) |
| x = torch.sigmoid(x) |
| return x |
| |
| def replacement(x): |
| return torch.neg(x) |
| |
| def comparison(x): |
| x = torch.neg(x) |
| return torch.neg(x) |
| |
| traced = symbolic_trace(f) |
| comparison_fn = symbolic_trace(comparison) |
| |
| x = torch.randn(3, 4) |
| |
| subgraph_rewriter.replace_pattern(traced, pattern, replacement) |
| |
| traced.graph.lint() |
| |
| ref_outs = comparison_fn(x) |
| test_outs = traced.forward(x) |
| self.assertEqual(ref_outs, test_outs) |
| |
| def test_subgraph_rewriter_replace_with_multiple_outputs(self): |
| |
| def f(x): |
| y = torch.sigmoid(x) |
| z = torch.relu(x) |
| return y + z |
| |
| def pattern(a): |
| b = torch.sigmoid(a) |
| c = torch.relu(a) |
| return b, c |
| |
| def replacement(x): |
| return torch.exp(x), torch.abs(x) |
| |
| def comparison(x): |
| y = torch.exp(x) |
| z = torch.abs(x) |
| return y + z |
| |
| traced = symbolic_trace(f) |
| comparison_fn = symbolic_trace(comparison) |
| |
| x = torch.randn(3, 4) |
| |
| subgraph_rewriter.replace_pattern(traced, pattern, replacement) |
| |
| traced.graph.lint() |
| |
| ref_outs = comparison_fn(x) |
| test_outs = traced.forward(x) |
| self.assertEqual(ref_outs, test_outs) |
| |
| def test_subgraph_rewriter_replace_with_duplicated_outputs(self): |
| |
| def f(x1, x2): |
| x = x1 - x2 |
| y = torch.sigmoid(x) |
| z = torch.relu(x) |
| return y + z |
| |
| def pattern(a1, a2): |
| a = a1 - a2 |
| b = torch.sigmoid(a) |
| c = torch.relu(a) |
| return b, c, a |
| |
| def replacement(x1, x2): |
| y1 = torch.exp(x1) |
| y2 = torch.abs(x2) |
| return y2, y2, y1 |
| |
| def comparison(x1, x2): |
| y2 = torch.abs(x2) |
| return y2 + y2 |
| |
| traced = symbolic_trace(f) |
| comparison_fn = symbolic_trace(comparison) |
| |
| x1 = torch.randn(3, 4) |
| x2 = torch.randn(3, 4) |
| |
| subgraph_rewriter.replace_pattern(traced, pattern, replacement) |
| |
| traced.graph.lint() |
| |
| ref_outs = comparison_fn(x1, x2) |
| test_outs = traced.forward(x1, x2) |
| self.assertEqual(ref_outs, test_outs) |
| |
| def test_subgraph_rewriter_with_unused_args(self): |
| class M(torch.nn.Module): |
| def forward(self, x, y, z): |
| return x + y |
| |
| def pattern(x, y): |
| return x + y |
| |
| def replacement(x, y): |
| return x - y |
| |
| def comparison(x1, x2, x3): |
| return x1 - x2 |
| |
| traced = symbolic_trace(M()) |
| comparison_fn = symbolic_trace(comparison) |
| |
| x1 = torch.randn(3, 4) |
| x2 = torch.randn(3, 4) |
| x3 = torch.randn(3, 4) |
| |
| subgraph_rewriter.replace_pattern(traced, pattern, replacement) |
| |
| traced.graph.lint() |
| placeholder_nodes = [n for n in traced.graph.nodes if n.op == "placeholder"] |
| assert len(placeholder_nodes) == 3 |
| |
| ref_outs = comparison_fn(x1, x2, x3) |
| test_outs = traced.forward(x1, x2, x3) |
| self.assertEqual(ref_outs, test_outs) |
| |
| def test_subgraph_rewriter_call_method(self): |
| |
| class M(torch.nn.Module): |
| def forward(self, x): |
| x = x.dequantize() |
| x = x.sigmoid() |
| x = x.to(torch.float16) |
| return x |
| |
| def pattern(x): |
| x = x.dequantize() |
| x = x.sigmoid() |
| x = x.to(torch.float16) |
| return x |
| |
| def replacement(x): |
| return x |
| |
| traced = symbolic_trace(M()) |
| comparison_fn = symbolic_trace(replacement) |
| |
| x1 = torch.randn(3, 4) |
| |
| subgraph_rewriter.replace_pattern(traced, pattern, replacement) |
| |
| traced.graph.lint() |
| |
| ref_outs = comparison_fn(x1) |
| test_outs = traced.forward(x1) |
| self.assertEqual(ref_outs, test_outs) |
| |
| def test_subgraph_rewriter_nodes_with_kwargs(self): |
| |
| class M(torch.nn.Module): |
| def __init__(self) -> None: |
| super().__init__() |
| self.w0 = torch.nn.Parameter(torch.empty([128, 128])) |
| self.b0 = torch.nn.Parameter(torch.empty([128])) |
| |
| def forward(self, in0): |
| lin_res = torch.nn.functional.linear(in0, self.w0, bias=self.b0) |
| mul_res = in0 * lin_res |
| sum_res = mul_res + in0 |
| return sum_res |
| |
| def pattern(a, b, bias): |
| lin_res = torch.nn.functional.linear(a, b, bias=bias) |
| mul_res = a * lin_res |
| return lin_res, mul_res |
| |
| def replacement(a, b, bias): |
| lin_res, mul_res = wrapped_gemm_bias_mul(a, b, bias) |
| return lin_res, mul_res |
| |
| traced = symbolic_trace(M()) |
| matches = subgraph_rewriter.replace_pattern(traced, pattern, replacement) |
| |
| self.assertEqual(len(matches), 1) |
| |
| found_repalcement_node = False |
| for node in traced.graph.nodes: |
| if node.target == wrapped_gemm_bias_mul: |
| found_repalcement_node = True |
| break |
| |
| self.assertTrue(found_repalcement_node) |
| |
| def test_subgraph_rewriter_local_revert(self): |
| |
| # Following model will have 3 anchors as the matching candidate with the given pattern |
| # Anchor 1 and 3 is a real match, but anchor 2 is not. |
| # The subgraph rewriter should be able to revert the changes made while matching anchor 2. |
| # Final match with anchor 3 should be successful. |
| |
| class M(torch.nn.Module): |
| def __init__(self) -> None: |
| super().__init__() |
| self.w0 = torch.nn.Parameter(torch.empty([128, 128])) |
| self.b0 = torch.nn.Parameter(torch.empty([128])) |
| self.w1 = torch.nn.Parameter(torch.empty([128, 128])) |
| self.b1 = torch.nn.Parameter(torch.empty([128])) |
| self.w2 = torch.nn.Parameter(torch.empty([128, 128])) |
| self.b2 = torch.nn.Parameter(torch.empty([128])) |
| self.w3 = torch.nn.Parameter(torch.empty([128, 128])) |
| self.b3 = torch.nn.Parameter(torch.empty([128])) |
| self.w4 = torch.nn.Parameter(torch.empty([128, 128])) |
| self.b4 = torch.nn.Parameter(torch.empty([128])) |
| |
| def forward(self, in0, in1): |
| lin_res_1 = torch.nn.functional.linear(in1, self.w0, bias=self.b0) |
| lin_res_2 = torch.nn.functional.linear(lin_res_1, self.w1, bias=self.b1) |
| # potential match at anchor 1 |
| mul_res_1 = in1 * lin_res_2 |
| sum_res_1 = mul_res_1 + in1 |
| lin_res_3 = torch.nn.functional.linear( |
| sum_res_1, self.w2, bias=self.b2 |
| ) |
| sigmoid_res_1 = torch.sigmoid(lin_res_3) |
| # potential match at anchor 2 |
| mul_res_2 = lin_res_3 * sigmoid_res_1 |
| lin_res_4 = torch.nn.functional.linear(in0, self.w3, bias=self.b3) |
| lin_res_5 = torch.nn.functional.linear(lin_res_4, self.w4, bias=self.b4) |
| # potential match at anchor 3 |
| mul_res_3 = in0 * lin_res_5 |
| sum_res_2 = mul_res_3 + in0 |
| cat_res = torch.cat( |
| [mul_res_2, sum_res_2], |
| dim=1, |
| ) |
| return cat_res |
| |
| def gemm_bias_mul_pattern_with_c(a, b, bias, c): |
| lin_res = torch.nn.functional.linear(a, b, bias=bias) |
| mul_res = c * lin_res |
| return lin_res, mul_res |
| |
| def gemm_bias_mul_replacement_with_c(a, b, bias, c): |
| lin_res, mul_res = wrapped_gemm_bias_mul_with_c(a, b, bias, c) |
| return lin_res, mul_res |
| |
| traced = symbolic_trace(M()) |
| matches = subgraph_rewriter.replace_pattern( |
| traced, |
| gemm_bias_mul_pattern_with_c, |
| gemm_bias_mul_replacement_with_c) |
| |
| self.assertEqual(len(matches), 2) |
| |
| repalcement_node_found = 0 |
| for node in traced.graph.nodes: |
| if node.target == wrapped_gemm_bias_mul_with_c: |
| repalcement_node_found += 1 |
| |
| self.assertEqual(repalcement_node_found, 2) |
| |
| def test_replace_pattern_with_filter(self): |
| class M(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| |
| def forward(self, x, scale, zero_point): |
| # Match, second input to add is a scalar |
| x = x.dequantize() |
| x = torch.add(x, 2) |
| x = x.relu() |
| x = torch.quantize_per_tensor(x, scale, zero_point, torch.quint8) |
| |
| y = x + 1 |
| # NOT a match, second input to add is NOT a scalar |
| x = x.dequantize() |
| x = torch.add(x, y) |
| x = x.relu() |
| x = torch.quantize_per_tensor(x, scale, zero_point, torch.quint8) |
| |
| return x |
| |
| def BinaryOpScalarReLUPattern(x, num, scale, zero_point): |
| x = x.dequantize() |
| x = torch.add(x, num) |
| x = x.relu() |
| x = torch.quantize_per_tensor(x, scale, zero_point, torch.quint8) |
| return x |
| |
| def BinaryOpScalarReLUReplacement(x, num, scale, zero_point): |
| x = torch.mul(x, num) |
| return x |
| |
| def second_input_is_scalar(match, original_graph, pattern_graph): |
| """ check the node that's matched to the second input of the pattern graph |
| is a scalar number |
| """ |
| input_idx = 0 |
| for node in pattern_graph.nodes: |
| if node.op == "placeholder": |
| if input_idx == 1: |
| num_node = node |
| input_idx += 1 |
| if not isinstance(match.nodes_map[num_node], (int, float)): |
| return False |
| return True |
| |
| def num_repalcement_node_found(traced): |
| return sum(1 for node in traced.graph.nodes if node.target == torch.mul) |
| |
| # match without filter, should find 2 match |
| traced = symbolic_trace(M()) |
| matches = subgraph_rewriter.replace_pattern( |
| traced, |
| BinaryOpScalarReLUPattern, |
| BinaryOpScalarReLUReplacement) |
| self.assertEqual(len(matches), 2) |
| self.assertEqual(num_repalcement_node_found(traced), 2) |
| |
| # match with filter, should find 1 match |
| traced = symbolic_trace(M()) |
| matches = subgraph_rewriter.replace_pattern_with_filter( |
| traced, |
| BinaryOpScalarReLUPattern, |
| BinaryOpScalarReLUReplacement, |
| second_input_is_scalar) |
| self.assertEqual(len(matches), 1) |
| self.assertEqual(num_repalcement_node_found(traced), 1) |
