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[Dynamo] Fix arg ordering in tf modes (#159707)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159707 Approved by: https://github.com/zou3519
This commit is contained in:
Michael Lazos
committed by
PyTorch MergeBot
PyTorch MergeBot
parent
e273ff028a
commit
9f8cfe7476
@ -717,10 +717,10 @@ TRACE FX call mul from test_logging.py:N in fn (LoggingTests.test_trace_call_pre
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self.assertExpectedInline(
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munge_shape_guards(record.getMessage()),
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"""\
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+- __SHAPE_GUARD__: L['x'].size()[0] == 2*L['z'].size()[0] # return x + torch.cat([y, z]) # #:# in # #:# in #
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+- __SHAPE_GUARD__: L['y'].size()[0] == L['z'].size()[0] # duck sizing added this equality because these variables had the same size 3 (to avoid this specialization, set torch.fx.experimental._config.use_duck_shape = False)
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+- __SHAPE_GUARD__: ((2*L['z'].size()[0]) % 3) == 0 # if x.size(0) % 3 == 0: # #:# in # #:# in #
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+- __SHAPE_GUARD__: 2 <= L['z'].size()[0] # return x + torch.cat([y, z]) # #:# in # (user code shown is first use of this value--the guard itself is not due user code but due to 0/1 specialization in the framework; to avoid specialization try torch._dynamo.mark_unbacked(tensor, dim))""", # noqa: B950
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+- __SHAPE_GUARD__: L['x'].size()[0] == 2*L['y'].size()[0] # return x + torch.cat([y, z]) # #:# in # #:# in #
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+- __SHAPE_GUARD__: L['z'].size()[0] == L['y'].size()[0] # duck sizing added this equality because these variables had the same size 3 (to avoid this specialization, set torch.fx.experimental._config.use_duck_shape = False)
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+- __SHAPE_GUARD__: ((2*L['y'].size()[0]) % 3) == 0 # if x.size(0) % 3 == 0: # #:# in # #:# in #
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+- __SHAPE_GUARD__: 2 <= L['y'].size()[0] # return x + torch.cat([y, z]) # #:# in # (user code shown is first use of this value--the guard itself is not due user code but due to 0/1 specialization in the framework; to avoid specialization try torch._dynamo.mark_unbacked(tensor, dim))""", # noqa: B950
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)
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@make_logging_test(guards=True)
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@ -319,11 +319,11 @@ class StructuredTraceTest(TestCase):
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{"dynamo_start": {"stack": "STACK"}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0}
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{"describe_storage": {"id": 0, "describer_id": "ID", "size": 4000000}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0}
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{"describe_tensor": {"id": 0, "ndim": 2, "dtype": "torch.float32", "device": "device(type='cpu')", "size": [1000, 1000], "is_leaf": true, "stride": [1000, 1], "storage": 0, "view_func": "VIEW_FUNC", "describer_id": "ID"}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0}
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{"describe_source": {"describer_id": "ID", "id": 0, "source": "L['y']"}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0}
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{"describe_source": {"describer_id": "ID", "id": 0, "source": "L['x']"}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0}
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{"describe_storage": {"id": 1, "describer_id": "ID", "size": 4000000}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0}
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{"describe_tensor": {"id": 1, "ndim": 2, "dtype": "torch.float32", "device": "device(type='cpu')", "size": [1000, 1000], "is_leaf": true, "stride": [1000, 1], "storage": 1, "view_func": "VIEW_FUNC", "describer_id": "ID"}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0}
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{"describe_source": {"describer_id": "ID", "id": 1, "source": "L['x']"}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0}
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{"dynamo_output_graph": {"sizes": {"l_y_": [1000, 1000], "l_x_": [1000, 1000], "add": [1000, 1000]}}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "HASH"}
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{"describe_source": {"describer_id": "ID", "id": 1, "source": "L['y']"}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0}
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{"dynamo_output_graph": {"sizes": {"l_x_": [1000, 1000], "l_y_": [1000, 1000], "add": [1000, 1000]}}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "HASH"}
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{"artifact": {"name": "before_pre_grad_graph", "encoding": "string"}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "HASH"}
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{"artifact": {"name": "after_pre_grad_graph", "encoding": "string"}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "HASH"}
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{"artifact": {"name": "aotautograd_cache_miss", "encoding": "json"}, "frame_id": 0, "frame_compile_id": 0, "attempt": 0, "has_payload": "HASH"}
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@ -8913,7 +8913,7 @@ def forward(self, p_lin_weight, p_lin_bias, x):
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self.assertExpectedInline(
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str(ep_decompose_linear.graph_module.code).strip(),
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"""\
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def forward(self, p_conv_weight, p_conv_bias, p_conv1d_weight, p_conv1d_bias, c_linear_bias, c_linear_weight, x, y):
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def forward(self, p_conv_weight, p_conv_bias, p_conv1d_weight, p_conv1d_bias, c_linear_weight, c_linear_bias, x, y):
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conv2d = torch.ops.aten.conv2d.default(x, p_conv_weight, p_conv_bias); x = p_conv_weight = p_conv_bias = None
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conv1d = torch.ops.aten.conv1d.default(y, p_conv1d_weight, p_conv1d_bias); y = p_conv1d_weight = p_conv1d_bias = None
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permute = torch.ops.aten.permute.default(c_linear_weight, [1, 0]); c_linear_weight = None
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@ -185,8 +185,7 @@ class AutoFunctionalizeTests(torch._inductor.test_case.TestCase):
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post_grad_graphs,
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"""\
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def forward(self, arg0_1: "f32[3][1]cpu", arg1_1: "f32[3][1]cpu", arg2_1: "f32[3][1]cpu", arg3_1: "f32[3][1]cpu", arg4_1: "f32[3][1]cpu"):
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# Custom comment for test
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foo_default = torch.ops.mylib.foo.default(arg2_1, [arg3_1, arg4_1], arg1_1, 2, arg0_1); arg2_1 = arg3_1 = arg4_1 = arg1_1 = arg0_1 = foo_default = None
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foo_default = torch.ops.mylib.foo.default(arg0_1, [arg3_1, arg4_1], arg1_1, 2, arg2_1); arg0_1 = arg3_1 = arg4_1 = arg1_1 = arg2_1 = foo_default = None
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return ()""", # noqa: B950
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ignore_comments=True,
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)
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@ -247,7 +246,7 @@ def forward(self, arg0_1: "f32[3][1]cpu", arg1_1: "f32[3][1]cpu", arg2_1: "f32[3
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post_grad_graphs,
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"""\
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def forward(self, arg0_1: "f32[3][1]cpu", arg1_1: "f32[3][1]cpu", arg2_1: "f32[3][1]cpu", arg3_1: "f32[3][1]cpu", arg4_1: "f32[3][1]cpu"):
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foo_default = torch.ops.mylib.foo.default(arg2_1, [arg3_1, arg4_1], arg1_1, 2, arg0_1); arg2_1 = arg3_1 = arg4_1 = arg1_1 = arg0_1 = None
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foo_default = torch.ops.mylib.foo.default(arg0_1, [arg3_1, arg4_1], arg1_1, 2, arg2_1); arg0_1 = arg3_1 = arg4_1 = arg1_1 = arg2_1 = None
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getitem_4: "f32[3][1]cpu" = foo_default[0]
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getitem_5: "f32[3][1]cpu" = foo_default[1]; foo_default = None
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return (getitem_4, getitem_5)""", # noqa: B950
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@ -334,9 +333,8 @@ def forward(self, arg0_1: "f32[3][1]cpu", arg1_1: "f32[3][1]cpu", arg2_1: "f32[3
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post_grad_graphs,
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"""\
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def forward(self, arg0_1: "f32[3][1]cpu", arg1_1: "f32[3][1]cpu", arg2_1: "f32[3][1]cpu", arg3_1: "f32[3][1]cpu"):
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# Custom comment for test
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foo_default = torch.ops.mylib.foo.default(None, [arg2_1, arg3_1], arg1_1, 2, arg0_1); \
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arg2_1 = arg3_1 = arg1_1 = arg0_1 = foo_default = None
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foo_default = torch.ops.mylib.foo.default(None, [arg2_1, arg3_1], arg0_1, 2, arg1_1); arg2_1 = \
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arg3_1 = arg0_1 = arg1_1 = foo_default = None
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return ()""",
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ignore_comments=True,
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)
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@ -416,10 +414,10 @@ arg2_1 = arg3_1 = arg1_1 = arg0_1 = foo_default = None
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self.assertExpectedInline(
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post_grad_graphs,
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"""\
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def forward(self, arg0_1: "Sym(s72)", arg1_1: "f32[s72][1]cpu", arg2_1: "f32[s72][1]cpu", arg3_1: "f32[s72][1]cpu", arg4_1: "f32[s72][1]cpu", arg5_1: "f32[s72][1]cpu"):
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foo_default = torch.ops.mylib.foo.default(arg3_1, [arg4_1, arg5_1], arg2_1, 2, arg1_1); arg4_1 = arg5_1 = arg1_1 = foo_default = None
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copy_: "f32[s72][1]cpu" = torch.ops.aten.copy_.default(arg2_1, arg2_1); arg2_1 = copy_ = None
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copy__1: "f32[s72][1]cpu" = torch.ops.aten.copy_.default(arg3_1, arg3_1); arg3_1 = copy__1 = None
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def forward(self, arg0_1: "Sym(s77)", arg1_1: "f32[s77][1]cpu", arg2_1: "f32[s77][1]cpu", arg3_1: "f32[s77][1]cpu", arg4_1: "f32[s77][1]cpu", arg5_1: "f32[s77][1]cpu"):
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foo_default = torch.ops.mylib.foo.default(arg1_1, [arg4_1, arg5_1], arg2_1, 2, arg3_1); arg4_1 = arg5_1 = arg3_1 = foo_default = None
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copy_: "f32[s77][1]cpu" = torch.ops.aten.copy_.default(arg1_1, arg1_1); arg1_1 = copy_ = None
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copy__1: "f32[s77][1]cpu" = torch.ops.aten.copy_.default(arg2_1, arg2_1); arg2_1 = copy__1 = None
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return ()""", # noqa: B950
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ignore_comments=True,
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ignore_empty_lines=True,
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@ -429,9 +427,9 @@ def forward(self, arg0_1: "Sym(s72)", arg1_1: "f32[s72][1]cpu", arg2_1: "f32[s72
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post_grad_graphs,
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"""\
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def forward(self, arg0_1: "f32[3][1]cpu", arg1_1: "f32[3][1]cpu", arg2_1: "f32[3][1]cpu", arg3_1: "f32[3][1]cpu", arg4_1: "f32[3][1]cpu"):
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foo_default = torch.ops.mylib.foo.default(arg2_1, [arg3_1, arg4_1], arg1_1, 2, arg0_1); arg3_1 = arg4_1 = arg0_1 = foo_default = None
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copy_: "f32[3][1]cpu" = torch.ops.aten.copy_.default(arg1_1, arg1_1); arg1_1 = copy_ = None
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copy__1: "f32[3][1]cpu" = torch.ops.aten.copy_.default(arg2_1, arg2_1); arg2_1 = copy__1 = None
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foo_default = torch.ops.mylib.foo.default(arg0_1, [arg3_1, arg4_1], arg1_1, 2, arg2_1); arg3_1 = arg4_1 = arg2_1 = foo_default = None
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copy_: "f32[3][1]cpu" = torch.ops.aten.copy_.default(arg0_1, arg0_1); arg0_1 = copy_ = None
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copy__1: "f32[3][1]cpu" = torch.ops.aten.copy_.default(arg1_1, arg1_1); arg1_1 = copy__1 = None
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return ()""", # noqa: B950
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ignore_comments=True,
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ignore_empty_lines=True,
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@ -523,11 +521,11 @@ def forward(self, arg0_1: "f32[3][1]cpu", arg1_1: "f32[3][1]cpu", arg2_1: "f32[3
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post_grad_graphs,
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"""\
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def forward(self, arg0_1: "f32[3][1]cpu", arg1_1: "f32[3][1]cpu", arg2_1: "f32[3][1]cpu", arg3_1: "f32[3][1]cpu", arg4_1: "f32[3][1]cpu"):
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foo_default = torch.ops.mylib.foo.default(arg2_1, [arg3_1, arg4_1], arg1_1, 2, arg0_1); arg3_1 = arg4_1 = arg0_1 = None
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foo_default = torch.ops.mylib.foo.default(arg0_1, [arg3_1, arg4_1], arg1_1, 2, arg2_1); arg3_1 = arg4_1 = arg2_1 = None
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getitem_4: "f32[3][1]cpu" = foo_default[0]
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getitem_5: "f32[3][1]cpu" = foo_default[1]; foo_default = None
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copy_: "f32[3][1]cpu" = torch.ops.aten.copy_.default(arg1_1, arg1_1); arg1_1 = copy_ = None
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copy__1: "f32[3][1]cpu" = torch.ops.aten.copy_.default(arg2_1, arg2_1); arg2_1 = copy__1 = None
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copy_: "f32[3][1]cpu" = torch.ops.aten.copy_.default(arg0_1, arg0_1); arg0_1 = copy_ = None
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copy__1: "f32[3][1]cpu" = torch.ops.aten.copy_.default(arg1_1, arg1_1); arg1_1 = copy__1 = None
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return (getitem_4, getitem_5)""", # noqa: B950
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ignore_comments=True,
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ignore_empty_lines=True,
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@ -581,13 +579,13 @@ def forward(self, arg0_1: "f32[3][1]cpu", arg1_1: "f32[3][1]cpu", arg2_1: "f32[3
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self.assertExpectedInline(
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graph_aot,
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"""\
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def forward(self, arg0_1: "Sym(s17)", arg1_1: "f32[s17][1]cpu", arg2_1: "f32[s17][1]cpu"):
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auto_functionalized_v2 = torch.ops.higher_order.auto_functionalized_v2(torch.ops.mylib.foo.default, _x_base_index = 0, _y_base_index = 1, _all_bases = [arg2_1, arg1_1])
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getitem_1: "f32[s17][1]cpu" = auto_functionalized_v2[1]
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getitem_2: "f32[s17][1]cpu" = auto_functionalized_v2[2]; auto_functionalized_v2 = None
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add: "f32[s17][1]cpu" = torch.ops.aten.add.Tensor(getitem_1, getitem_2)
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copy_: "f32[s17][1]cpu" = torch.ops.aten.copy_.default(arg1_1, getitem_2); arg1_1 = getitem_2 = copy_ = None
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copy__1: "f32[s17][1]cpu" = torch.ops.aten.copy_.default(arg2_1, getitem_1); arg2_1 = getitem_1 = copy__1 = None
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def forward(self, arg0_1: "Sym(s77)", arg1_1: "f32[s77][1]cpu", arg2_1: "f32[s77][1]cpu"):
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auto_functionalized_v2 = torch.ops.higher_order.auto_functionalized_v2(torch.ops.mylib.foo.default, _x_base_index = 0, _y_base_index = 1, _all_bases = [arg1_1, arg2_1])
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getitem_1: "f32[s77][1]cpu" = auto_functionalized_v2[1]
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getitem_2: "f32[s77][1]cpu" = auto_functionalized_v2[2]; auto_functionalized_v2 = None
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add: "f32[s77][1]cpu" = torch.ops.aten.add.Tensor(getitem_1, getitem_2)
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copy_: "f32[s77][1]cpu" = torch.ops.aten.copy_.default(arg1_1, getitem_1); arg1_1 = getitem_1 = copy_ = None
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copy__1: "f32[s77][1]cpu" = torch.ops.aten.copy_.default(arg2_1, getitem_2); arg2_1 = getitem_2 = copy__1 = None
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return (add,)""", # noqa: B950
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ignore_comments=True,
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ignore_empty_lines=True,
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@ -597,12 +595,12 @@ def forward(self, arg0_1: "Sym(s17)", arg1_1: "f32[s17][1]cpu", arg2_1: "f32[s17
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graph_aot,
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"""\
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def forward(self, arg0_1: "f32[2][1]cpu", arg1_1: "f32[2][1]cpu"):
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auto_functionalized_v2 = torch.ops.higher_order.auto_functionalized_v2(torch.ops.mylib.foo.default, _x_base_index = 0, _y_base_index = 1, _all_bases = [arg1_1, arg0_1])
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auto_functionalized_v2 = torch.ops.higher_order.auto_functionalized_v2(torch.ops.mylib.foo.default, _x_base_index = 0, _y_base_index = 1, _all_bases = [arg0_1, arg1_1])
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getitem_1: "f32[2][1]cpu" = auto_functionalized_v2[1]
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getitem_2: "f32[2][1]cpu" = auto_functionalized_v2[2]; auto_functionalized_v2 = None
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add: "f32[2][1]cpu" = torch.ops.aten.add.Tensor(getitem_1, getitem_2)
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copy_: "f32[2][1]cpu" = torch.ops.aten.copy_.default(arg0_1, getitem_2); arg0_1 = getitem_2 = copy_ = None
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copy__1: "f32[2][1]cpu" = torch.ops.aten.copy_.default(arg1_1, getitem_1); arg1_1 = getitem_1 = copy__1 = None
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copy_: "f32[2][1]cpu" = torch.ops.aten.copy_.default(arg0_1, getitem_1); arg0_1 = getitem_1 = copy_ = None
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copy__1: "f32[2][1]cpu" = torch.ops.aten.copy_.default(arg1_1, getitem_2); arg1_1 = getitem_2 = copy__1 = None
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return (add,)""", # noqa: B950
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ignore_comments=True,
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ignore_empty_lines=True,
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@ -613,11 +611,11 @@ def forward(self, arg0_1: "f32[2][1]cpu", arg1_1: "f32[2][1]cpu"):
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self.assertExpectedInline(
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graph_inductor,
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"""\
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def forward(self, arg0_1: "Sym(s17)", arg1_1: "f32[s17][1]cpu", arg2_1: "f32[s17][1]cpu"):
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foo_default = torch.ops.mylib.foo.default(arg2_1, arg1_1); foo_default = None
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add: "f32[s17][1]cpu" = torch.ops.aten.add.Tensor(arg2_1, arg1_1)
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copy_: "f32[s17][1]cpu" = torch.ops.aten.copy_.default(arg1_1, arg1_1); arg1_1 = copy_ = None
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copy__1: "f32[s17][1]cpu" = torch.ops.aten.copy_.default(arg2_1, arg2_1); arg2_1 = copy__1 = None
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def forward(self, arg0_1: "Sym(s77)", arg1_1: "f32[s77][1]cpu", arg2_1: "f32[s77][1]cpu"):
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foo_default = torch.ops.mylib.foo.default(arg1_1, arg2_1); foo_default = None
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add: "f32[s77][1]cpu" = torch.ops.aten.add.Tensor(arg1_1, arg2_1)
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copy_: "f32[s77][1]cpu" = torch.ops.aten.copy_.default(arg1_1, arg1_1); arg1_1 = copy_ = None
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copy__1: "f32[s77][1]cpu" = torch.ops.aten.copy_.default(arg2_1, arg2_1); arg2_1 = copy__1 = None
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return (add,)""",
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ignore_comments=True,
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ignore_empty_lines=True,
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@ -627,8 +625,8 @@ def forward(self, arg0_1: "Sym(s17)", arg1_1: "f32[s17][1]cpu", arg2_1: "f32[s17
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graph_inductor,
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"""\
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def forward(self, arg0_1: "f32[2][1]cpu", arg1_1: "f32[2][1]cpu"):
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foo_default = torch.ops.mylib.foo.default(arg1_1, arg0_1); foo_default = None
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add: "f32[2][1]cpu" = torch.ops.aten.add.Tensor(arg1_1, arg0_1)
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foo_default = torch.ops.mylib.foo.default(arg0_1, arg1_1); foo_default = None
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add: "f32[2][1]cpu" = torch.ops.aten.add.Tensor(arg0_1, arg1_1)
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copy_: "f32[2][1]cpu" = torch.ops.aten.copy_.default(arg0_1, arg0_1); arg0_1 = copy_ = None
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copy__1: "f32[2][1]cpu" = torch.ops.aten.copy_.default(arg1_1, arg1_1); arg1_1 = copy__1 = None
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return (add,)""",
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@ -843,11 +841,11 @@ def forward(self, arg0_1: "f32[2][1]cpu"):
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graph_aot,
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"""\
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def forward(self, arg0_1: "f32[2][1]cpu", arg1_1: "f32[2][1]cpu"):
|
||||
auto_functionalized_v2 = torch.ops.higher_order.auto_functionalized_v2(torch.ops.mylib.foo.default, _x_base_index = 0, _y_base_index = 1, _all_bases = [arg1_1, arg0_1])
|
||||
auto_functionalized_v2 = torch.ops.higher_order.auto_functionalized_v2(torch.ops.mylib.foo.default, _x_base_index = 0, _y_base_index = 1, _all_bases = [arg0_1, arg1_1])
|
||||
getitem_1: "f32[2][1]cpu" = auto_functionalized_v2[1]
|
||||
getitem_2: "f32[2][1]cpu" = auto_functionalized_v2[2]; auto_functionalized_v2 = None
|
||||
copy_: "f32[2][1]cpu" = torch.ops.aten.copy_.default(arg0_1, getitem_2); arg0_1 = getitem_2 = copy_ = None
|
||||
copy__1: "f32[2][1]cpu" = torch.ops.aten.copy_.default(arg1_1, getitem_1); arg1_1 = getitem_1 = copy__1 = None
|
||||
copy_: "f32[2][1]cpu" = torch.ops.aten.copy_.default(arg0_1, getitem_1); arg0_1 = getitem_1 = copy_ = None
|
||||
copy__1: "f32[2][1]cpu" = torch.ops.aten.copy_.default(arg1_1, getitem_2); arg1_1 = getitem_2 = copy__1 = None
|
||||
return ()""", # noqa: B950
|
||||
ignore_comments=True,
|
||||
ignore_empty_lines=True,
|
||||
@ -859,7 +857,7 @@ def forward(self, arg0_1: "f32[2][1]cpu", arg1_1: "f32[2][1]cpu"):
|
||||
graph_inductor,
|
||||
"""\
|
||||
def forward(self, arg0_1: "f32[2][1]cpu", arg1_1: "f32[2][1]cpu"):
|
||||
foo_default = torch.ops.mylib.foo.default(arg1_1, arg0_1); foo_default = None
|
||||
foo_default = torch.ops.mylib.foo.default(arg0_1, arg1_1); foo_default = None
|
||||
copy_: "f32[2][1]cpu" = torch.ops.aten.copy_.default(arg0_1, arg0_1); arg0_1 = copy_ = None
|
||||
copy__1: "f32[2][1]cpu" = torch.ops.aten.copy_.default(arg1_1, arg1_1); arg1_1 = copy__1 = None
|
||||
return ()""", # noqa: B950
|
||||
@ -979,8 +977,8 @@ def forward(self, arg0_1: "f32[2][1]cpu", arg1_1: "f32[2][1]cpu", arg2_1: "f32[2
|
||||
post_grad_graphs,
|
||||
"""\
|
||||
def forward(self, arg0_1: "f32[3][1]cpu", arg1_1: "f32[3][1]cpu", arg2_1: "f32[3][1]cpu", arg3_1: "f32[3][1]cpu"):
|
||||
foo_default = torch.ops.mylib.foo.default(None, [arg2_1, arg3_1], arg1_1, 2, arg0_1); arg2_1 = arg3_1 = arg0_1 = foo_default = None
|
||||
copy_: "f32[3][1]cpu" = torch.ops.aten.copy_.default(arg1_1, arg1_1); arg1_1 = copy_ = None
|
||||
foo_default = torch.ops.mylib.foo.default(None, [arg2_1, arg3_1], arg0_1, 2, arg1_1); arg2_1 = arg3_1 = arg1_1 = foo_default = None
|
||||
copy_: "f32[3][1]cpu" = torch.ops.aten.copy_.default(arg0_1, arg0_1); arg0_1 = copy_ = None
|
||||
return ()""", # noqa: B950
|
||||
ignore_comments=True,
|
||||
ignore_empty_lines=True,
|
||||
|
||||
@ -1333,8 +1333,8 @@ class TestMaxAutotune(TestCase):
|
||||
if not TEST_WITH_ROCM:
|
||||
expected = """{
|
||||
'input_nodes':[
|
||||
"[[s77,s17],[s17,1],torch.float32,device(type='cuda',index=0),0]",
|
||||
"[[s17,s94],[s94,1],torch.float32,device(type='cuda',index=0),0]"],
|
||||
"[[s77,s27],[s27,1],torch.float32,device(type='cuda',index=0),0]",
|
||||
"[[s27,s94],[s94,1],torch.float32,device(type='cuda',index=0),0]"],
|
||||
'num_stages':1,'num_warps':2,'prefix_args':0,'suffix_args':0,'call_sizes':[s77,s94],
|
||||
'layout':"[[s77,s94],[s94,1],torch.float32,device(type='cuda',index=0),0]",'num_consumer_groups':0,
|
||||
'num_buffers_warp_spec':0,'epilogue_fn_hash':'identity','kwargs':{'EVEN_K':False,'ALLOW_TF32':True,
|
||||
|
||||
@ -389,7 +389,7 @@ def _flatten_vts(vts):
|
||||
output = []
|
||||
|
||||
while vts:
|
||||
vt = vts.pop()
|
||||
vt = vts.popleft()
|
||||
|
||||
if not vt.is_realized() and vt.peek_type() in (dict, list, tuple):
|
||||
vt.realize()
|
||||
@ -397,8 +397,10 @@ def _flatten_vts(vts):
|
||||
if vt.is_realized():
|
||||
if isinstance(vt, ListVariable):
|
||||
vts.extend(vt.items)
|
||||
continue
|
||||
elif isinstance(vt, ConstDictVariable):
|
||||
vts.extend(vt.items.values())
|
||||
continue
|
||||
|
||||
output.append(vt)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user